mlsanityData to 14 Oct 2026

Differentiable Physics-informed Graph Networks

arXiv 1902.02950 ↗First posted 8 Feb 2019cs.LG stat.MLcited by 25

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Abstract (arXiv)

While physics conveys knowledge of nature built from an interplay between observations and theory, it has been considered less importantly in deep neural networks. Especially, there are few works leveraging physics behaviors when the knowledge is given less explicitly. In this work, we propose a novel architecture called Differentiable Physics-informed Graph Networks (DPGN) to incorporate implicit physics knowledge which is given from domain experts by informing it in latent space. Using the concept of DPGN, we demonstrate that climate prediction tasks are significantly improved. Besides the experiment results, we validate the effectiveness of the proposed module and provide further applications of DPGN, such as inductive learning and multistep predictions.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Feb 2019.

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Citing papers per year: 2021: 9; 2022: 8; 2023: 3; 2024: 4; 2025: 1.

25 papers.

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  1. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 367
  2. 2022Multimodal learning with graphscited by 54
  3. 2021Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learningcited by 52
  4. 2021Latent Space Data Assimilation by using Deep Learningcited by 30
  5. 2023Physics-Informed Machine Learning for Modeling and Control of Dynamical Systemscited by 28
  6. 2022Scalable algorithms for physics-informed neural and graph networkscited by 21
  7. 2021Climate Modeling with Neural Diffusion Equationscited by 20
  8. 2024PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systemscited by 18
  9. 2023Graph Neural Network-based surrogate model for granular flowscited by 16
  10. 2021Neural Pharmacodynamic State Space Modelingcited by 12
  11. 2023Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrologycited by 11
  12. 2022PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Networkcited by 5
13 more
  1. 2021Neural Weighted A*: Learning Graph Costs and Heuristics with Differentiable Anytime A*cited by 3
  2. 2022Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive Biasescited by 3
  3. 2024Beyond Grid Data: Exploring Graph Neural Networks for Earth Observationcited by 3
  4. 2021Model discovery in the sparse sampling regimecited by 2
  5. 2021Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systemscited by 2
  6. 2022Physics-Informed Graph Learningcited by 2
  7. 2024GN-SINDy: Greedy Sampling Neural Network in Sparse Identification of Nonlinear Partial Differential Equationscited by 2
  8. 2021ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecastingcited by 1
  9. 2022AI Research Associate for Early-Stage Scientific Discoverycited by 1
  10. 2022Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulationcited by 1
  11. 2025Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Latercited by 1
  12. 2022Unraveled Multilevel Transformation Networks for Predicting Sparsely-Observed Spatiotemporal Dynamicscited by 0
  13. 2024Global Prediction of COVID-19 Variant Emergence Using Dynamics-Informed Graph Neural Networkscited by 0