Differentiable Physics

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Latest papers 36

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NeuralBES: A Differentiable, Control-Aware Emulator for Scalable Building Energy Modeling
Oct 7, 2026Ting-Yu Dai, Takuya Kurihana, Wing Yee Au +1Neural Surrogate Modeling Differentiable Physics
Fujitsu Research of America
UWB Meets Crazyflow: Simulating Degraded Feedback at Scale for Aerial Robotics
Oct 6, 2026Martin Schuck, Marcel P. Rath, Yufei Hua +3Aerial Robotics Robotics Simulation
Learning Systems and Robotics Lab, Technical University of Munich, Germany
Physics-based Sphere Packing for Lagrangian Mesh Morphing
Oct 6, 2026Jiong Lin, Hod Lipson3D Mesh Generation Differentiable Physics
Columbia University
ProCut: Probabilistic Cutting Topology for Autonomous Electrosurgical Tissue Dissection
Oct 5, 2026Xiao Liang, Fei Liu, Florian Richter +2Differentiable Physics Surgical Robotics
DexForge: High-Fidelity Physics-Informed Dexterous Retargeting
Oct 5, 2026Meizhong Wang, Kun Cao, Ruiqi Ni +2Contact-Rich Robotic Manipulation Motion Retargeting
Tongji University · Shanghai Research Institute for Intelligent Autonomous Systems · Purdue University +1
PowerSim: Differentiable Physics Simulation and Rendering with Power Diagrams
Sep 29, 2026Trong-Tung Nguyen, Anand BhattadDifferentiable Rendering 3D Scene Representation
Johns Hopkins University
On the Numerical Reliability of Differentiable Physics-Based Optimization for Robotic Material Manipulation
Sep 28, 2026Xintong Yang, Minglun Wei, Yu-Kun Lai +1Robot Dynamics Identification Material Point Method
School of Engineering, Cardiff University, Cardiff, United Kingdom · School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom · College of Mechanical and Electrical Engineering, Hohai University, China
Ostrich: Taking Large Strides Through Stiff Contact in Differentiable Dynamics
Sep 8, 2026Aleš Kučera, Karel ZimmermannTrajectory Optimization Contact-Rich Manipulation
Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
Differentiable Hybrid Modelling for Learning and Optimising Chemical Transport Processes from Experimental Data
Sep 3, 2026Arthur Jessop, Mohammed Alsubeihi, Ben Moseley +1Differentiable Physics Differentiable Optimization
Department of Chemical Engineering, The University of Manchester, Manchester, M13 9PL, United Kingdom. · Department of Earth Science and Engineering, Imperial College London, South Kensington, SW7 2BX, United Kingdom. · I-X Centre for AI in Science, Imperial College London, South Kensington, SW7 2BX, United Kingdom.
Differentiate the Solver, Not the Equation: Reverse-Sweep Adjoints for Block Implicit Simulation
Aug 9, 2026Lei Shu, Ying Jiang, Kui Wu +3Automatic Differentiation Differentiable Programming
Stanford University · University of California, Los Angeles · LightSpeed Studios, Tencent America +1
SoRoMoX: Fast, Differentiable, and Parallelizable Soft Robot Models
Aug 6, 2026Maximilian Stölzle, Solange Gribonval, Daniel Feliu-Talegon +9Differentiable Physics Continuum Robot Control
Delft University of Technology, Delft, The Netherlands · Italian Institute of Technology, Genoa, Italy · Massachusetts Institute of Technology, Cambridge, MA, USA +1
BendTwin: Robust Dense-to-Sparse Physical Reconstruction with Bending-Aware Differentiable Spring-Mass Models
Aug 6, 2026Yixiong Jing, Qi Wang, Lin Chen +6Structural Mechanics Sparse-View 3D Reconstruction
University of Cambridge · Institute of Automation, Chinese Academy of Sciences · Northwestern Polytechnical University +1
PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics
Jul 22, 2026Haocheng Yin, Shuohan Tao, Yongsheng Chen +1World Models for Robotics Deformable Object Manipulation
Georgia Institute of Technology
Wearing A Coat: Dual-Arm Robot-Assisted Dressing with Differentiable Clothing Simulation
Jul 13, 2026Yiming Liu, Lijun Han, Hesheng WangContact-Rich Robotic Manipulation Assistive Robotics
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Microcosmos: Reimagining Artificial Life for the GPU Era
Jul 3, 2026Mark Tensen, Ciaran Regan, Bert Wang-Chak Chan +3Artificial Life Differentiable Physics
1Artificial Life Institute, Japan · 2Sakana AI, Japan · University of Tsukuba, Japan +2
Mosaic: A Benchmark Suite for Differentiable Physics Solvers
Jun 26, 2026Andrin Rehmann, Heiko Zimmermann, Dion HäfnerAutomatic Differentiation PDE Solving
Pasteur Labs, Brooklyn, NY, USA
URDF Synthesis from RGB-D Sequences via Differentiable Joint Inference and Energy-Consistent Verification
Jun 17, 2026Xinze ZhangArticulated Object Reconstruction 3D Reconstruction
University of Southern California, Los Angeles, CA 90007, USA
Robustness without Wrinkles: Parallel Simulation and Robust MPC for Certified Deformable Manipulation
Jun 12, 2026Wei-Chen Li, Jeffrey Fang, Sasanka Polisetti +2Constrained Motion Planning Contact-Rich Robotic Manipulation
Georgia Institute of Technology, Atlanta, GA 30308
Beyond the Thin-Layer Limit: Differentiable Volumetric Training for Visible-Range Diffractive Neural Networks
Jun 5, 2026Dineth Jayakody, Dushan N. WadduwageDifferentiable Physics Differentiable Optimization
Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA · School of Data Science, Old Dominion University, Norfolk, VA 23529, USA · Department of Physics, Old Dominion University, Norfolk, VA 23529, USA
Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks
Jun 4, 2026Mahmoud Elhadidy, Siva Viknesh, Roshan M. D'Souza +1Differentiable Physics Physics-Informed ML
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, USA · Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA · Department of Mechanical Engineering, University of Wisconsin–Milwaukee, Milwaukee, WI, USA +1
DLO-Lab: Benchmarking Deformable Linear Object Manipulations with Differentiable Physics
Jun 2, 2026Junyi Cao, Yian Wang, Ziyan Xiong +3Deformable Object Manipulation Robot Manipulation Benchmarks
University of Massachusetts Amherst
Critical evaluation of PINN for FWD inverse analysis and differentiable FEM as an alternative
Jun 2, 2026Yongjin Choi, Hyeonbin Moon, Seunghwa RyuDifferentiable Physics Physics-Informed ML
KAIST InnoCORE PRISM-AI Center, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea · Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea · School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, USA
Crazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX
May 31, 2026Martin Schuck, Marcel P. Rath, Yufei Hua +3Aerial Robotics Robotics Simulation
1Technical University of Munich · University of Toronto · 3Simon Fraser University
Batched Differentiable Rigid Body Dynamics in PyTorch for GPU-Accelerated Robot Learning
May 29, 2026Yue Wang, Yanran Xu, Wenbo Wu +2Multibody Dynamics Robot Dynamics Learning
University of Southampton, Southampton, SO17 1BJ, UK
DiffPhD: A Unified Differentiable Solver for Projective Heterogeneous Materials in Elastodynamics with Contact-Rich GPU-Acceleration
May 14, 2026Shih-Yu Lai, Sung-Han Tien, Jui-I Huang +9Robotics Simulation Hyperelasticity
National Taiwan University · MoonShine Animation Studio · National University of Singapore +3
Real2Sim: A Physics-driven and Editable Gaussian Splatting Framework for Autonomous Driving Scenes
May 13, 2026Kaicong Huang, Talha Azfar, Weisong Shi +13D Gaussian Splatting Synthetic Data Generation
Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY USA 12180. · Department of Computer and Information Sciences, University of Delaware, Newark, DE USA 19716.
OrbiSim: World Models as Differentiable Physics Engines for Embodied Intelligence
May 12, 2026Jiajian Li, Jingyuan Huang, Junru Gong +3Robotic Control Robotics Simulation
MoE Key Lab of Artificial Intelligence, AI Institute, School of Computer Science Shanghai Jiao Tong University
Differentiable Parameter Optimization for DAEs with State-Dependent Events
May 6, 2026Ion Matei, Maksym Zhenirovskyy, Anthony WongDynamical Systems Automatic Differentiation
Fujitsu Research of America
Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perception
May 6, 2026Yuhu Guo, Zhikai Shen, Jiasheng Qu +4Neural Surrogate Modeling Material Point Method
The University of Manchester, UK · The Chinese University of Hong Kong, Hong Kong SAR, China · University of Leeds, UK +1
Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems
May 6, 2026Miloš Babić, Franz M. Rohrhofer, Stefan PoschDynamical Systems Differentiable Physics
CD Laboratory for Physics-driven Machine Learning in Industrial Applications, Graz, Austria · The Institute of Thermodynamics and Sustainable Propulsion Systems, Graz University of Technology, Graz, Austria · Know Center Research GmbH, Graz, Austria

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

NeuralBES: A Differentiable, Control-Aware Emulator for Scalable Building Energy Modeling

Ting-Yu Dai, Takuya Kurihana, Wing Yee Au, Hon Yung Wong

Fujitsu Research of America

Neural Surrogate Modeling Differentiable Physics

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Demand-side flexibility i.e. forecasting, shifting, and curtailing residential energy loads, depends on thermal models trusted across millions of heterogeneous buildings. Existing tools force a hard tradeoff: high-fidelity physics simulators such as EnergyPlus are accurate but sequential and require per-building calibration, while purely data-driven sequence models scale but abandon the physical structure that makes their predictions trustworthy. We introduce NeuralBES (Building Energy Simulation), a differentiable emulator that resolves this tradeoff by parameterizing a resistance--capacitance (RC) based thermal model with a shared neural encoder: static building metadata such as floor area, vintage, and HVAC type is mapped to physically bounded capacitances, conductances, and equipment coefficients, which become the coefficients of a scalar linear recurrence solved via a log-space parallel scan, and a predictor--corrector loop closes the thermostat--temperature nonlinearity while preserving full-horizon gradient flow. Trained on the ResStock dataset across three climate zones, NeuralBES handles heterogeneous building archetypes, vintages, and climate zones within a single trained encoder, while black-box baselines produce statistically plausible but physically inconsistent trajectories. On the annual full-year rollout, NeuralBES is the only data-conditioned model that is simultaneously physics-valid and accurate to within 4 MAPE points of the strongest raw-error baseline, while operating at roughly an order of magnitude fewer parameters than the transformer and recurrent baselines; among physics-valid baselines at parameter parity it more than halves the MAPE of the grey-box RC alternative.

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Latest papers 36