Physics-Informed Forecasting

Latest papers

20 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

IMPACT: Inverter-Informed Multimodal Learning for Photovoltaic Generation Forecasting
Oct 29, 2026Anushka Katiyar, Cong Feng, Jing Wang +3Physics-Informed Forecasting Solar Power Forecasting
National Laboratory of the Rockies · Latimer Controls
The Utility of Physics-Informed Machine Learning in Solar Power Production Forecasting
Oct 28, 2026Peter Pavlík, Franz M. Rohrhofer, Bernhard C. GeigerPhysics-Informed Forecasting Solar Power Forecasting
Kempelen Institute of Intelligent Technologies, Sky Park Offices, Bottova 7939/2A, Bratislava, 811 09, Slovakia · Faculty of Information Technology, Brno University of Technology, Božetěchova 2, Brno, 612 00, Czech Republic · Know Center Research GmbH, Sandgasse 34, Graz, 8010, Austria +2
PING: physics informed graph neural network for advection-diffusion processes with applications to solar power forecasting
Oct 13, 2026Jelena Simeunović, Baptiste Schubnel, Pierre-Jean Alet +2Physics-Informed GNNs Physics-Informed Forecasting
CSEM, Rue de Jaquet-Droz 1, 2002 Neuchâtel, Switzerland · Department of Electrical Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Rte Cantonale, 1015 Lausanne, Switzerland.
Physics-Refined Spatiotemporal Forecasting on Open-Boundary Hydrologic Graphs
Oct 1, 2026Haoyang Jiang, Zhengui Wang, Shenghan Gao +3Spatiotemporal Forecasting Physics-Informed GNNs
William & Mary, Williamsburg, VA, USA · Virginia Institute of Marine Science, William & Mary, Gloucester Point, VA, USA · Florida Atlantic University, Boca Raton, FL, USA
Physical knowledge on historical data matters more than enforcing physical constraints on the forecast
Sep 17, 2026Etienne Lehembre, Pascal Audigane, Vincent Nguyen +2Physics-Informed Forecasting Time Series Forecasting
CA, LIFO · BRGM · LIFO +1
MZ-Rain: Moisture-Budget-Guided Zero-Inflated Model for Station-Level Precipitation Nowcasting
Sep 4, 2026Yifang Zhang, Shengwu Xiong, Henan Wang +6Physics-Informed Forecasting Precipitation Nowcasting
Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya, 572000, China · School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China · School of Earth and Space Science and Technology, Wuhan University, Wuhan 430072, China +2
Climate Physics Dynamic Matching
Aug 27, 2026Gurjeet Sangra Singh, Frantzeska Lavda, Alexandros KalousisWeather Forecasting Physics-Informed Generative Modeling
University of Geneva, Geneva, Switzerland · HES-SO Geneva, Geneva, Switzerland
Quantum Reservoir Computing with Physics-Informed Correction for Reduced-Order PDE Forecasting
Aug 24, 2026Krishna Bhatia, Harsh, Shalini DevendrababuQuantum Reservoir Computing Physics-Informed Forecasting
QuantumAI Lab, Fractal Analytics, Mumbai, India · Department of Physics, Indian Institute of Technology, Jodhpur, India
ExPhy: A Benchmark for Explicit Physical Property Learning in Multi-Object Trajectory Forecasting
Aug 20, 2026Rui Wang, Yeteng Wu, Xianlin Zhang +1Trajectory Prediction Forecasting Benchmarks
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications Beijing, China · School of Digital Media & Design Art, Beijing University of Posts and Telecommunications Beijing, China
PhysAttNet: Enhancing Predictive Performance in Industrial and Astrophysical Time Series via Physics-Informed Attention
Aug 7, 2026Amal Saadallah, Julia Tjus, Petra Wiederkeher +1Time Series Forecasting Physics-Informed ML
Faculty of Physics and Astronomy, Ruhr University Bochum, Germany · Virtual Machining Group, TU Dortmund, Germnay · Faculty of Physics, Germany
Phys-JEPA: Physics-Informed Latent World Models for Multivariate Time-Series Forecasting
Jun 15, 2026Weizhi Nie, Weichao Liu, Honglin Guo +1Multivariate Time Series Forecasting Latent Dynamics Modeling
Tianjin University
Seeing Inside the Storm: Improving Nowcasting by Integrating Meteorological Drivers
May 22, 2026Minghui Qiu, Jun Chen, Lin Chen +5Precipitation Forecasting Physics-Informed Forecasting
HKUST(GZ) · Guangzhou Meteorological Observatory
PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting
May 16, 2026Yangyou Liu, Zezhi Shao, Xinyu Chen +3OOD Generalization Time Series Generation
College of Computer Science, Sichuan University, Chengdu, China · Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · Institute of Artificial Intelligence, University of Central Florida, Orlando, USA +1
Exactness Matters for Physical Rule Enforcement
May 8, 2026Bum Jun KimTime Series Forecasting Spatiotemporal Forecasting
The University of Tokyo, Japan
PINN-Cast: Exploring the Role of Continuous-Depth NODE in Transformers and Physics Informed Loss as Soft Physical Constraints in Short-term Weather Forecasting
Apr 30, 2026Hira Saleem, Flora Salim, Cormac PurcellTransformer Physics-Informed ML
University of New South Wales, Sydney, NSW, Australia
A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers
Apr 14, 2026Mohammad AlShaikh Saleh, Sanjay Chawla, Sertac Bayhan +2Physics-Informed Forecasting Time Series Forecasting
College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar · Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar · Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha, Qatar +1
DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting
Apr 8, 2026Yeran Zhang, Pengwei Yang, Guoqing Wang +1Physics-Informed Forecasting Time Series Forecasting
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China · Research Center, East Hope Group Co., Ltd, Shanghai, China
Cross-Resolution Attention Network for High-Resolution PM2.5 Prediction
Mar 12, 2026Ammar Kheder, Helmi Toropainen, Wenqing Peng +3Physics-Informed Forecasting Air Quality Prediction
Department of Computational Engineering, LUT University, Finland · Atmospheric Modelling Centre Lahti (AMC-Lahti), Finland · Institute for Atmospheric and Earth System Research, University of Helsinki, P.O. Box 64, Helsinki 00014, Finland +1
MAD-SmaAt-GNet: A Multimodal Advection-Guided Neural Network for Precipitation Nowcasting
Mar 3, 2026Samuel van Wonderen, Siamak MehrkanoonPhysics-Informed Forecasting Precipitation Forecasting
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
PHAST: Port-Hamiltonian Architecture for Structured Temporal Dynamics Forecasting
Feb 20, 2026Shubham Bhardwaj, Chandrajit BajajTime Series Forecasting Physics-Informed Forecasting
Department of Computer Science, Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA

Oct 29, 2026J/K move · Enter open · S save

IMPACT: Inverter-Informed Multimodal Learning for Photovoltaic Generation Forecasting

Anushka Katiyar, Cong Feng, Jing Wang, Tristan Liu+2

National Laboratory of the Rockies · Latimer Controls

Physics-Informed Forecasting Solar Power Forecasting Probabilistic Time Series Forecasting Multivariate Time Series Forecasting

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This work develops a physics-informed multimodal deep learning framework for 10- to 110-minute-ahead probabilistic photovoltaic power forecasting. The forecasting model is built on a four-branch architecture that separately encodes plant-level temporal history and inverter-level spatial cloud dynamics through a graph attention encoder with a learned adjacency, known-future solar geometry, and the current meteorological state, fused through gated cross-attention. The geometry branch adds two forward-looking physical signals, spatial cloud heterogeneity and inverter ramp-down activity, each empirically weighted toward the horizon where it is most predictive. The decoder anchors its prediction to a smart persistence baseline, learning a horizon-conditioned correction, and the resulting quantile forecasts are calibrated online through a kernel-weighted adaptive conformal calibration step. The proposed model outperforms all 14 baselines, achieving a forecast skill score (FSS) of 0.2950, 0.099 above the next-best model. A branch-composition ablation shows that every branch contributes positively and the model retains substantial skill in challenging overcast and variable sky conditions (FSS 0.198 and 0.255).