Physics-Informed Diffusion Models

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This field studies how to make diffusion models generate or recover data that obey physical laws, especially when observations are sparse, noisy, or incomplete. Common approaches add PDE residuals, conservation laws, or boundary conditions to training, or enforce constraints during sampling with differentiable solvers, projections, and measurement guidance. Applications include scientific field generation and forecasting, inverse problems such as imaging and seismic reconstruction, and physically plausible motion. A central tension is improving physical consistency without distorting the data distribution, while keeping inference efficient and uncertainty reliable.

Latest papers

51 papers8 papers in the last four weeks, up 167% on the four weeks before. 0.0% of all new papers.

Apr 10, 2026

Vector Field Synthesis with Sparse Streamlines Using Diffusion Model

We present a novel diffusion-based framework for synthesizing 2D vector fields from sparse, coherent inputs (i.e., streamlines) while maintaining physical plausibility. Our method employs a conditional denoising diffusion probabilistic model with classifier-free guidance, enabling progressive reconstruction that preserves both geometric and physical constraints. Experimental results demonstrate our method's ability to synthesize plausible vector fields that adhere to physical laws while maintaining fidelity to sparse input observations, outperforming traditional optimization-based approaches in terms of flexibility and physical consistency.

Nguyen K. Phan, Ricardo Morales, Sebastian D. Espriella +1

University of Houston

Physics-Informed Diffusion Models

Apr 10, 2026

PDE-regularized Dynamics-informed Diffusion with Uncertainty-aware Filtering for Long-Horizon Dynamics

Long-horizon spatiotemporal prediction remains a challenging problem due to cumulative errors, noise amplification, and the lack of physical consistency in existing models. While diffusion models provide a probabilistic framework for modeling uncertainty, conventional approaches often rely on mean squared error objectives and fail to capture the underlying dynamics governed by physical laws. In this work, we propose PDYffusion, a dynamics-informed diffusion framework that integrates PDE-based regularization and uncertainty-aware forecasting for stable long-term prediction. The proposed method consists of two key components: a PDE-regularized interpolator and a UKF-based forecaster. The interpolator incorporates a differential operator to enforce physically consistent intermediate states, while the forecaster leverages the Unscented Kalman Filter to explicitly model uncertainty and mitigate error accumulation during iterative prediction. We provide theoretical analyses showing that the proposed interpolator satisfies PDE-constrained smoothness properties, and that the forecaster converges under the proposed loss formulation. Extensive experiments on multiple dynamical datasets demonstrate that PDYffusion achieves superior performance in terms of CRPS and MSE, while maintaining stable uncertainty behavior measured by SSR. We further analyze the inherent trade-off between prediction accuracy and uncertainty, showing that our method provides a balanced and robust solution for long-horizon forecasting.

Min Young Baeg, Yoon-Yeong Kim

Spatiotemporal Forecasting Long-Term Time Series Forecasting Physics-Informed Diffusion Models Probabilistic Time Series Forecasting

Apr 7, 2026

Single-Stage Signal Attenuation Diffusion Model for Low-Light Image Enhancement and Denoising

Diffusion models excel at image restoration via probabilistic modeling of forward noise addition and reverse denoising, and their ability to handle complex noise while preserving fine details makes them well-suited for Low-Light Image Enhancement (LLIE). Mainstream diffusion based LLIE methods either adopt a two-stage pipeline or an auxiliary correction network to refine U-Net outputs, which severs the intrinsic link between enhancement and denoising and leads to suboptimal performance owing to inconsistent optimization objectives. To address these issues, we propose the Signal Attenuation Diffusion Model (SADM), a novel diffusion process that integrates the signal attenuation mechanism into the diffusion pipeline, enabling simultaneous brightness adjustment and noise suppression in a single stage. Specifically, the signal attenuation coefficient simulates the inherent signal attenuation of low-light degradation in the forward noise addition process, encoding the physical priors of low-light degradation to explicitly guide reverse denoising toward the concurrent optimization of brightness recovery and noise suppression, thereby eliminating the need for extra correction modules or staged training relied on by existing methods. We validate that our design maintains consistency with Denoising Diffusion Implicit Models(DDIM) via multi-scale pyramid sampling, balancing interpretability, restoration quality, and computational efficiency.

Ying Liu, Junchao Zhang, Caiyun Wu

School of Aeronautics and Astronautics, Central South University, Changsha 410083, China · Hunan Provincial Key Laboratory of Optic-Electronic Intelligent Measurement and Control, Changsha 410083, China

Low-Light Image Enhancement Diffusion Models for Image Restoration Image Denoising Physics-Informed Diffusion Models

Apr 4, 2026

PhaseFlow4D: Physically Constrained 4D Beam Reconstruction via Feedback-Guided Latent Diffusion

We address the problem of recovering a time-varying 4D distribution from a sparse sequence of 2D projections - analogous to novel-view synthesis from sparse cameras, but applied to the 4D transverse phase space density

ρ(x,p_x,y,p_y)

of charged particle beams. Direct single shot measurement of this high-dimensional distribution is physically impossible in real particle accelerator systems; only limited 1D or 2D projections are accessible. We propose PhaseFlow4D, a feedback-guided latent diffusion model that reconstructs and tracks the full 4D phase space from incomplete 2D observations alone, with built-in hard physics constraints. Our core technical contribution is a 4D VAE whose decoder generates the full 4D phase space tensor, from which 2D projections are analytically computed and compared against 2D beam measurements. This projection-consistency constraint guarantees physical correctness by construction - not as a soft penalty, but as an architectural prior. An adaptive feedback loop then continuously tunes the conditioning vector of the latent diffusion model to track time-varying distributions online without retraining. We validate on multi-particle simulations of heavy-ion beams at the Facility for Rare Isotope Beams (FRIB), where full physics simulations require

\sim

6 hours on a 100-core HPC system. PhaseFlow4D achieves accurate 4D reconstructions 11000

\times

faster while faithfully tracking distribution shifts under time-varying source conditions - demonstrating that principled generative reconstruction under incomplete observations transfers robustly beyond visual domains.

Alexander Scheinker, Alexander Plastun, Peter Ostroumov

Los Alamos National Laboratory Los Alamos 87545, NM, USA · Facility for Rare Isotopes Beams Michigan State University, East Lansing, MI 48824, USA

4D Reconstruction Physics-Informed Diffusion Models Latent Diffusion Models High-Energy Physics

Apr 3, 2026

MMPhysVideo: Physically Plausible Video Generation Through Joint RGB-Perception Modeling

Despite advancements in generating visually stunning content, video diffusion models (VDMs) often yield physically inconsistent results due to pixel-only reconstruction. To address this, we propose MMPhysVideo, the first study to enhance physical plausibility in video generation through joint multimodal modeling. We recast perceptual cues, specifically semantics, geometry, and spatio-temporal trajectories, into a unified pseudo-RGB format, enabling VDMs to directly capture complex physical dynamics. To mitigate cross-modal interference, we propose a Bidirectionally Controlled Teacher architecture, which utilizes parallel branches to fully decouple RGB and perception processing and adopts two zero-initialized control links to gradually establish pixel-wise consistency. For inference efficiency, the teacher's physical prior is distilled into a single-stream student model via representation alignment. Furthermore, we present MMPhysPipe, an end-to-end data curation and annotation pipeline tailored for constructing physics-rich multimodal datasets. MMPhysPipe employs a vision-language model (VLM) guided by a chain-of-visual-evidence rule to pinpoint physical subjects, enabling expert models to extract multi-granular perceptual information. Without additional inference costs, MMPhysVideo consistently improves physical plausibility of advanced models on the Videophy and PhyGenbench benchmarks and achieves superior performance among existing methods.

Shubo Lin, Xuanyang Zhang, Wei Cheng +3

State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA · School of Artificial Intelligence, University of Chinese Academy of Sciences · StepFun +2

Physical Consistency in Video Generation Video Diffusion Models Physics-Informed Generative Modeling Physics-Informed Diffusion Models Multimodal Diffusion Models

Mar 27, 2026

PAD-Hand: Physics-Aware Diffusion for Hand Motion Recovery

Significant advancements made in reconstructing hands from images have delivered accurate single-frame estimates, yet they often lack physics consistency and provide no notion of how confidently the motion satisfies physics. In this paper, we propose a novel physics-aware conditional diffusion framework that refines noisy pose sequences into physically plausible hand motion while estimating the physics variance in motion estimates. Building on a MeshCNN-Transformer backbone, we formulate Euler-Lagrange dynamics for articulated hands. Unlike prior works that enforce zero residuals, we treat the resulting dynamic residuals as virtual observables to more effectively integrate physics. Through a last-layer Laplace approximation, our method produces per-joint, per-time variances that measure physics consistency and offers interpretable variance maps indicating where physical consistency weakens. Experiments on two well-known hand datasets show consistent gains over strong image-based initializations and competitive video-based methods. Qualitative results confirm that our variance estimations are aligned with the physical plausibility of the motion in image-based estimates.

Elkhan Ismayilzada, Yufei Zhang, Zijun Cui

Michigan State University · Independent Researcher

3D Hand Pose Estimation Physics-Informed Diffusion Models Articulated Object Reconstruction Uncertainty Quantification

Mar 19, 2026

RadioDiff-FS: Physics-Informed Manifold Alignment in Few-Shot Diffusion Models for High-Fidelity Radio Map Construction

Radio maps (RMs) provide spatially continuous propagation characterizations essential for 6G network planning, but high-fidelity RM construction remains challenging. Rigorous electromagnetic solvers incur prohibitive computational latency, while data-driven models demand massive labeled datasets and generalize poorly from simplified simulations to complex multipath environments. This paper proposes RadioDiff-FS, a few-shot diffusion framework that adapts a pretrained main-path generator to multipath-rich target domains with only a small number of high-fidelity samples. The adaptation is grounded in a theoretical decomposition of the multipath RM into a dominant main-path component and a directionally sparse residual. This decomposition shows that the cross-domain shift corresponds to a bounded and geometrically structured feature translation rather than an arbitrary distribution change. A direction-consistency loss (DCL) is then introduced to constrain diffusion score updates along physically plausible propagation directions, thereby suppressing phase-inconsistent artifacts that arise in the low-data regime. Experiments show that RadioDiff-FS reduces NMSE by 59.5% on static RMs and by 74.0% on dynamic RMs relative to the vanilla diffusion baseline, achieving an SSIM of 0.9752 and a PSNR of 36.37 dB under severely limited supervision. Even in a one-shot setting with a single target-domain sample per scene, RadioDiff-FS outperforms all fully supervised baselines, confirming that the directional constraint provides an effective inductive bias under extreme data scarcity. Code is available at https://github.com/UNIC-Lab/RadioDiff-FS.

Xiucheng Wang, Zixuan Guo, Nan Cheng

State Key Laboratory of ISN and School of Telecommunications Engineering, Xidian University, Xi’an 710071, China · School of Physics, Xidian University, Xi’an 710071, China

Radio Propagation Modeling Physics-Informed Diffusion Models Diffusion Model Fine-Tuning Few-Shot Learning

Mar 17, 2026

Robust Physics-Guided Diffusion for Full-Waveform Inversion

We develop a robust physics-guided diffusion framework for full-waveform inversion that combines a score-based generative prior with likelihood guidance computed through wave-equation simulations. We adopt a transport-based data-consistency potential (Wasserstein-2), incorporating wavefield enhancement via bounded weighting and observation-dependent normalization, thereby improving robustness to amplitude imbalance and time/phase misalignment. On the inference side, we introduce a preconditioned guided reverse-diffusion scheme that adapts the guidance strength and spatial scaling throughout the reverse-time dynamics, yielding a more stable and effective data-consistency guidance step than standard diffusion posterior sampling (DPS). Numerical experiments on OpenFWI datasets demonstrate improved reconstruction quality over deterministic optimization baselines and standard DPS under comparable computational budgets.

Jishen Peng, Enze Jiang, Zheng Ma +1

School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China · Institute of Natural Sciences, MOE-LSC, Shanghai Jiao Tong University, Shanghai, China · Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai, China +2

Diffusion-Based Inverse Problems Inverse Problems Physics-Informed Diffusion Models Diffusion Posterior Sampling

Mar 16, 2026

Learning Latent Proxies for Controllable Single-Image Relighting

Single-image relighting is highly under-constrained: small illumination changes can produce large, nonlinear variations in shading, shadows, and specularities, while geometry and materials remain unobserved. Existing diffusion-based approaches either rely on intrinsic or G-buffer pipelines that require dense and fragile supervision, or operate purely in latent space without physical grounding, making fine-grained control of direction, intensity, and color unreliable. We observe that a full intrinsic decomposition is unnecessary and redundant for accurate relighting. Instead, sparse but physically meaningful cues, indicating where illumination should change and how materials should respond, are sufficient to guide a diffusion model. Based on this insight, we introduce LightCtrl that integrates physical priors at two levels: a few-shot latent proxy encoder that extracts compact material-geometry cues from limited PBR supervision, and a lighting-aware mask that identifies sensitive illumination regions and steers the denoiser toward shading relevant pixels. To compensate for scarce PBR data, we refine the proxy branch using a DPO-based objective that enforces physical consistency in the predicted cues. We also present ScaLight, a large-scale object-level dataset with systematically varied illumination and complete camera-light metadata, enabling physically consistent and controllable training. Across object and scene level benchmarks, our method achieves photometrically faithful relighting with accurate continuous control, surpassing prior diffusion and intrinsic-based baselines, including gains of up to +2.4 dB PSNR and 35% lower RMSE under controlled lighting shifts.

Haoze Zheng, Zihao Wang, Xianfeng Wu +5

HKUST · HKPolyU · CUHK

Physics-Informed Diffusion Models Controllable Image Generation Diffusion-Based Image Generation Image-to-Image Translation

Mar 15, 2026

Mapping Dark-Matter Clusters via Physics-Guided Diffusion Models

Galaxy clusters are powerful probes of astrophysics and cosmology through gravitational lensing: the clusters' mass, dominated by 85% dark matter, distorts background light. Yet, mass reconstruction lacks the scalability and large-scale benchmarks to process the hundreds of thousands of clusters expected from forthcoming wide-field surveys. We introduce a fully automated method to reconstruct cluster surface mass density from photometry and gravitational lensing observables. Central to our approach is DarkClusters-15k, our new dataset of 15,000 simulated clusters with paired mass and photometry maps, the largest benchmark to date, spanning multiple redshifts and simulation frameworks. We train a plug-and-play diffusion prior on DarkClusters-15k that learns the statistical relationship between mass and light, and draw posterior samples constrained by weak- and strong-lensing observables; this yields principled reconstructions driven by explicit physics, alongside well-calibrated uncertainties. Our approach requires no expert tuning, runs in minutes rather than hours, achieves higher accuracy, and matches expertly-tuned reconstructions of the MACS 1206 cluster. We release our method and DarkClusters-15k to support development and benchmarking for upcoming wide-field cosmological surveys.

Diego Royo, Brandon Zhao, Adolfo Muñoz +2

Universidad de Zaragoza–I3A, Spain · California Institute of Technology, USA

Astrophysics Diffusion-Based Inverse Problems Physics-Informed Diffusion Models

Mar 14, 2026

CT-Conditioned Diffusion Prior with Physics-Constrained Sampling for PET Super-Resolution

PET super-resolution is highly under-constrained because paired multi-resolution scans from the same subject are rarely available, and effective resolution is determined by scanner-specific physics (e.g., PSF, detector geometry, and acquisition settings). This limits supervised end-to-end training and makes purely image-domain generative restoration prone to hallucinated structures when anatomical and physical constraints are weak. We formulate PET super-resolution as posterior inference under heterogeneous system configurations and propose a CT-conditioned diffusion framework with physics-constrained sampling. During training, a conditional diffusion prior is learned from high-quality PET/CT pairs using cross-attention for anatomical guidance, without requiring paired LR--HR PET data. During inference, measurement consistency is enforced through a scanner-aware forward model with explicit PSF effects and gradient-based data-consistency refinement. Under both standard and OOD settings, the proposed method consistently improves experimental metrics and lesion-level clinical relevance indicators over strong baselines, while reducing hallucination artifacts and improving structural fidelity.

Liutao Yang, Zi Wang, Peiyuan Jing +5

Department of Bioengineering and Imperial-X, Imperial College London, London, U.K. · College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, China. · School of Engineering, Zurich University of Applied Sciences, Winterthur, Switzerland. +3

Medical Image Super-Resolution PET/CT Imaging Physics-Informed Diffusion Models Diffusion Posterior Sampling

Mar 13, 2026

PhysMoDPO: Physically-Plausible Humanoid Motion with Preference Optimization

Recent progress in text-conditioned human motion generation has been largely driven by diffusion models trained on large-scale human motion data. Building on this progress, recent methods attempt to transfer such models for character animation and real robot control by applying a Whole-Body Controller (WBC) that converts diffusion-generated motions into executable trajectories. While WBC trajectories become compliant with physics, they may expose substantial deviations from original motion. To address this issue, we here propose PhysMoDPO, a Direct Preference Optimization framework. Unlike prior work that relies on hand-crafted physics-aware heuristics such as foot-sliding penalties, we integrate WBC into our training pipeline and optimize diffusion model such that the output of WBC becomes compliant both with physics and original text instructions. To train PhysMoDPO we deploy physics-based and task-specific rewards and use them to assign preference to synthesized trajectories. Our extensive experiments on text-to-motion and spatial control tasks demonstrate consistent improvements of PhysMoDPO in both physical realism and task-related metrics on simulated robots. Moreover, we demonstrate that PhysMoDPO results in significant improvements when applied to zero-shot motion transfer in simulation and for real-world deployment on a G1 humanoid robot.

Yangsong Zhang, Anujith Muraleedharan, Rikhat Akizhanov +5

Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) · LIGM, École des Ponts, IP Paris, Univ Gustave Eiffel, CNRS · École polytechnique fédérale de Lausanne (EPFL)

Human Motion Generation Robot Motion Generation Diffusion Models for Motion Generation Diffusion Model Alignment Physics-Informed Diffusion Models Direct Preference Optimization

Mar 10, 2026

Differentiable Stochastic Traffic Dynamics: Physics-Informed Generative Modelling in Transportation

Macroscopic traffic flow is stochastic, but the physics-informed deep learning methods currently used in transportation literature embed deterministic PDEs and produce point-valued outputs; the stochasticity of the governing dynamics plays no role in the learned representation. This work develops a framework in which the physics constraint itself is distributional and directly derived from stochastic traffic-flow dynamics. Starting from an Ito-type Lighthill-Whitham-Richards model with Brownian forcing, we derive a one-point forward equation for the marginal traffic density at each spatial location. The spatial coupling induced by the conservation law appears as an explicit conditional drift term, which makes the closure requirement transparent. Based on this formulation, we derive an equivalent deterministic Probability Flow ODE that is pointwise evaluable and differentiable once a closure is specified. Incorporating this as a physics constraint, we then propose a score network with an advection-closure module, trainable by denoising score matching together with a Fokker-Planck residual loss. The resulting model targets a data-conditioned density distribution, from which point estimates, credible intervals, and congestion-risk measures can be computed. The framework provides a basis for distributional traffic-state estimation and for stochastic fundamental-diagram analysis in a physics-informed generative setting.

Wuping Xin

Caliper Corporation

Physics-Informed Generative Modeling Traffic Flow Estimation Physics-Informed Diffusion Models Traffic Flow Theory Stochastic Differential Equations Fokker–Planck Equation

Mar 6, 2026

Physics-Informed Diffusion Model for Generating Synthetic Extreme Rare Weather Events Data

Data scarcity is a primary obstacle in developing robust Machine Learning (ML) models for detecting rapidly intensifying tropical cyclones. Traditional data augmentation techniques (rotation, flipping, brightness adjustment) fail to preserve the physical consistency and high-intensity gradients characteristic of rare Category 4-equivalent events, which constitute only 0.14% of our dataset (202 of 140,514 samples). We propose a physics-informed diffusion model based on the Context-UNet architecture to generate synthetic, multi-spectral satellite imagery of extreme weather events. Our model is conditioned on critical atmospheric parameters such as average wind speed, type of Ocean and stage of development (early, mature, late etc) -- the known drivers of rapid intensification. Using a controlled pre-generated noise sampling strategy and mixed-precision training, we generated

16\times16

wind-field samples that are cropped from multi-spectral satellite imagery which preserve realistic spatial autocorrelation and physical consistency. Results demonstrate that our model successfully learns discriminative features across ten distinct context classes, effectively mitigating the data bottleneck. Specifically, we address the extreme class imbalance in our dataset, where Class 4 (Ocean 2, early stage with average wind speed 50kn hurricane) contains only 202 samples compared to 79,768 samples in Class 0. This generative framework provides a scalable solution for augmenting training datasets for operational weather detection algorithms. The average Results yield an average Log-Spectral Distance (LSD) of 4.5dB, demonstrating a scalable framework for enhancing operational weather detection algorithms.

Marawan Yakout, Tannistha Maiti, Monira Majhabeen +1

Department of Computer Science, University of London, London · Deepkapha AI Lab, Assen, The Netherlands

Synthetic Data Generation Synthetic Data Augmentation Physics-Informed Diffusion Models Conditional Diffusion Models

Mar 5, 2026

Particle-Guided Diffusion for Gas-Phase Reaction Kinetics

Physics-guided sampling with diffusion model priors has shown promise for solving partial differential equation (PDE) governed problems, but applications to chemically meaningful reaction-transport systems remain limited. We apply diffusion-based guided sampling to gas-phase chemical reactions by training on solutions of the advection-reaction-diffusion (ARD) equation across varying parameters. The method generates physically consistent concentration fields and accurately predicts outlet concentrations, including at unseen parameter values, demonstrating the potential of diffusion models for inference in reactive transport.

Andrew Millard, Henrik Pedersen

Department of Computer and Information Science, Linköping University, Linköping, Sweden · Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden

Reaction-Diffusion Systems Physics-Informed Diffusion Models Diffusion Guidance

Mar 2, 2026

Zero-shot Low-Field MRI Enhancement via Diffusion-Based Adaptive Contrast Transport

Low-field (LF) magnetic resonance imaging (MRI) democratizes access to diagnostic imaging but is fundamentally limited by low signal-to-noise ratio and significant tissue contrast distortion due to field-dependent relaxation dynamics. Reconstructing high-field (HF) quality images from LF data is a blind inverse problem, severely challenged by the scarcity of paired training data and the unknown, non-linear contrast transformation operator. Existing zero-shot methods, which assume simplified linear degradation, often fail to recover authentic tissue contrast. In this paper, we propose DACT(Diffusion-Based Adaptive Contrast Transport), a novel zero-shot framework that restores HF-quality images without paired supervision. DACT synergizes a pre-trained HF diffusion prior to ensure anatomical fidelity with a physically-informed adaptive forward model. Specifically, we introduce a differentiable Sinkhorn optimal transport module that explicitly models and corrects the intensity distribution shift between LF and HF domains during the reverse diffusion process. This allows the framework to dynamically learn the intractable contrast mapping while preserving topological consistency. Extensive experiments on simulated and real clinical LF datasets demonstrate that DACT achieves state-of-the-art performance, yielding reconstructions with superior structural detail and correct tissue contrast.

Muyu Liu, Chenhe Du, Xuanyu Tian +5

School of Information Science and Technology, ShanghaiTech University, Shanghai, China · School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

Magnetic Resonance Imaging Diffusion Models for Image Restoration Diffusion-Based Inverse Problems Physics-Informed Diffusion Models

Feb 28, 2026

Phys-Diff: A Physics-Inspired Latent Diffusion Model for Tropical Cyclone Forecasting

Tropical cyclone (TC) forecasting is critical for disaster warning and emergency response. Deep learning methods address computational challenges but often neglect physical relationships between TC attributes, resulting in predictions lacking physical consistency. To address this, we propose Phys-Diff, a physics-inspired latent diffusion model that disentangles latent features into task-specific components (trajectory, pressure, wind speed) and employs cross-task attention to introduce prior physics-inspired inductive biases, thereby embedding physically consistent dependencies among TC attributes. Phys-Diff integrates multimodal data including historical cyclone attributes, ERA5 reanalysis data, and FengWu forecast fields via a Transformer encoder-decoder architecture, further enhancing forecasting performance. Experiments demonstrate state-of-the-art performance on global and regional datasets.

Lei Liu, Xiaoning Yu, Kang Chen +4

University of Science and Technology of China, Hefei, China · Hefei University of Technology, Hefei, China

Tropical Cyclone Intensity Forecasting Weather Forecasting Physics-Informed Diffusion Models Latent Diffusion Models Spatiotemporal Forecasting Multivariate Time Series Forecasting

Feb 27, 2026

Diffusion-Based Low-Light Image Enhancement with Color and Luminance Priors

Low-light images often suffer from low contrast, noise, and color distortion, degrading visual quality and impairing downstream vision tasks. We propose a novel conditional diffusion framework for low-light image enhancement that incorporates a Structured Control Embedding Module (SCEM). SCEM decomposes a low-light image into four informative components including illumination, illumination-invariant features, shadow priors, and color-invariant cues. These components serve as control signals that condition a U-Net-based diffusion model trained with a simplified noise-prediction loss. Thus, the proposed SCEM equipped Diffusion method enforces structured enhancement guided by physical priors. In experiments, our model is trained only on the LOLv1 dataset and evaluated without fine-tuning on LOLv2-real, LSRW, DICM, MEF, and LIME. The method achieves state-of-the-art performance in quantitative and perceptual metrics, demonstrating strong generalization across benchmarks. https://casted.github.io/scem/.

Xuanshuo Fu, Lei Kang, Javier Vazquez-Corral

Computer Vision Center, Cerdanyola del Vallès, Spain and Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain

Low-Light Image Enhancement Conditional Diffusion Models Physics-Informed Diffusion Models

Feb 25, 2026

Physics-Consistent Diffusion for Efficient Fluid Super-Resolution via Multiscale Residual Correction

Existing image SR and generic diffusion models transfer poorly to fluid SR: they are sampling-intensive, ignore physical constraints, and often yield spectral mismatch and spurious divergence. We address fluid super-resolution (SR) with ReMD (Residual-Multigrid Diffusion), a physics-consistent diffusion framework. At each reverse step, ReMD performs a multigrid residual correction: the update direction is obtained by coupling data consistency with lightweight physics cues and then correcting the residual across scales; the multiscale hierarchy is instantiated with a multi-wavelet basis to capture both large structures and fine vortical details. This coarse-to-fine design accelerates convergence and preserves fine structures while remaining equation-free. Across atmospheric and oceanic benchmarks, ReMD improves accuracy and spectral fidelity, reduces divergence, and reaches comparable quality with markedly fewer sampling steps than diffusion baselines. Our results show that enforcing physics consistency inside the diffusion process via multigrid residual correction and multi-wavelet multiscale modeling is an effective route to efficient fluid SR. Our code are available on https://github.com/lizhihao2022/ReMD.

Zhihao Li, Shengwei Dong, Chuang Yi +5

The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · SandGold AI Research, Guangzhou, China · The Hong Kong University of Science and Technology, Hong Kong SAR, China +1

Physics-Informed Diffusion Models Image Super-Resolution Diffusion Model Acceleration Computational Fluid Dynamics

Feb 19, 2026

Learning Flow Distributions via Projection-Constrained Diffusion on Manifolds

We present a generative modeling framework for synthesizing physically feasible two-dimensional incompressible flows under arbitrary obstacle geometries and boundary conditions. Whereas existing diffusion-based flow generators either ignore physical constraints, impose soft penalties that do not guarantee feasibility, or specialize to fixed geometries, our approach integrates three complementary components: (1) a boundary-conditioned diffusion model operating on velocity fields; (2) a physics-informed training objective incorporating a divergence penalty; and (3) a projection-constrained reverse diffusion process that enforces exact incompressibility through a geometry-aware Helmholtz-Hodge operator. We derive the method as a discrete approximation to constrained Langevin sampling on the manifold of divergence-free vector fields, providing a connection between modern diffusion models and geometric constraint enforcement in incompressible flow spaces. Experiments on analytic Navier-Stokes data and obstacle-bounded flow configurations demonstrate significantly improved divergence, spectral accuracy, vorticity statistics, and boundary consistency relative to unconstrained, projection-only, and penalty-only baselines. Our formulation unifies soft and hard physical structure within diffusion models and provides a foundation for generative modeling of incompressible fields in robotics, graphics, and scientific computing.

Noah Trupin, Rahul Ghosh, Aadi Jangid

Department of Computer Science, Purdue University

Constrained Generative Modeling Physics-Informed Diffusion Models Computational Fluid Dynamics

May 28, 2025

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation

Modeling physical systems in a generative manner offers several advantages, including the ability to handle partial observations, generate diverse solutions, and address both forward and inverse problems. Recently, diffusion models have gained increasing attention in the modeling of physical systems, particularly those governed by partial differential equations (PDEs). However, diffusion models only access noisy data

\boldsymbol{x}_t

at intermediate steps, making it infeasible to directly enforce constraints on the clean sample

\boldsymbol{x}_0

at each noisy level. As a workaround, constraints are typically applied to the expectation of clean samples

\mathbb{E}[\boldsymbol{x}_0|\boldsymbol{x}_t]

, which is estimated using the learned score network. However, imposing PDE constraints on the expectation does not strictly represent the one on the true clean data, known as Jensen's Gap. This gap creates a trade-off: enforcing PDE constraints may come at the cost of reduced accuracy in generative modeling. To address this, we propose a simple yet effective post-hoc distillation approach, where PDE constraints are not injected directly into the diffusion process, but instead enforced during a post-hoc distillation stage. We term our method as Physics-Informed Distillation of Diffusion Models (PIDDM). This distillation not only facilitates single-step generation with improved PDE satisfaction, but also support both forward and inverse problem solving and reconstruction from randomly partial observation. Extensive experiments across various PDE benchmarks demonstrate that PIDDM significantly improves PDE satisfaction over several recent and competitive baselines, such as PIDM, DiffusionPDE, and ECI-sampling, with less computation overhead. Our approach can shed light on more efficient and effective strategies for incorporating physical constraints into diffusion models.

Yi Zhang, Peng Wang, Difan Zou

Institute of Data Science · Department of Computer and Information Science · Institute of Data Science & School of Computing and Data Science

Physics-Informed Diffusion Models Diffusion Model Distillation Generative Modeling Physics-Informed ML PDE Inverse Problems