Physics-Informed ML papers, 2023–2026

Key findings

• Physics-Informed ML went from 50 papers in 2023 to 199 in 2026, roughly 4.0x in three years.
• NeurIPS publishes the most Physics-Informed ML work: 167 papers from 2023 to 2026, 35% of the four-venue total.
• The fastest-growing overlap is with Multimodal LLMs: papers tagged with both rose from 0 in 2023 to 11 in 2026.
• Among the 5 topics under Climate and Physical Sciences, it ranks #1 by 2026 paper count; the fastest grower in the group, Weather Forecasting, is up 6.1x versus 4.0x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Derivative-Informed Training of Neural Operators On-the-Fly via Sketched Tangent Consistency
• ICML 2026Bias-Spectrum Neural Processes for Parametric PDEs: Architecture Priors Meet PDE Constraints
• ICLR 2026CONSIGN: Conformal Segmentation Informed by Spatial Groupings via Decomposition
• CVPR 2026Domain Sensitive Federated Learning with Fisher-Informed Pruning
• NeurIPS 2025Activation-Informed Merging of Large Language Models
• ICML 2025Geometry-Informed Neural Networks
• ICLR 2025Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints
• CVPR 2025A Physics-Informed Blur Learning Framework for Imaging Systems
• NeurIPS 2024ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction
• ICML 2024A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction
• ICLR 2024AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction
• CVPR 2024A Physics-informed Low-rank Deep Neural Network for Blind and Universal Lens Aberration Correction
• NeurIPS 2023ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
• ICML 2023Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks
• ICLR 2023Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients
• CVPR 20233D Human Pose Estimation via Intuitive Physics
• NeurIPS 2026Neural Modal Decomposition: Architectural Priors from Observables
• ICML 2026Fine-to-Coarse Fairness-Informed Multi-View Clustering
• ICLR 2026From Neural Networks to Logical Theories: The Correspondence between Fibring Modal Logics and Fibring Neural Networks
• CVPR 2026LaDy Lagrangian-Dynamic Informed Network for Skeleton-based Action Segmentation via Spatial-Temporal

All Physics-Informed ML papers

50 papers, page 1 of 1

1. CVPR 20233D Human Pose Estimation via Intuitive Physics
2. CVPR 2023DP-NeRF: Deblurred Neural Radiance Field With Physical Scene Priors
3. CVPR 2023Imitation Learning As State Matching via Differentiable Physics
4. CVPR 2023Learning 3D Scene Priors With 2D Supervision
5. CVPR 2023Neural Part Priors: Learning To Optimize Part-Based Object Completion in RGB-D Scans
6. CVPR 2023NLOST: Non-Line-of-Sight Imaging With Transformer
7. CVPR 2023Physics-Driven Diffusion Models for Impact Sound Synthesis From Videos
8. CVPR 2023Thermal Spread Functions (TSF): Physics-Guided Material Classification
9. CVPR 2023Towards Unbiased Volume Rendering of Neural Implicit Surfaces With Geometry Priors
10. ICLR 2023Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients
11. ICLR 2023Competitive Physics Informed Networks
12. ICLR 2023DiffMimic: Efficient Motion Mimicking with Differentiable Physics
13. ICLR 2023Guiding continuous operator learning through Physics-based boundary constraints
14. ICLR 2023Improved Training of Physics-Informed Neural Networks Using Energy-Based Priors: a Study on Electrical Impedance Tomography
15. ICLR 2023NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning
16. ICLR 2023Phase2vec: dynamical systems embedding with a physics-informed convolutional network
17. ICLR 2023Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth
18. ICLR 2023Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation
19. ICML 2023Achieving High Accuracy with PINNs via Energy Natural Gradient Descent
20. ICML 2023Bayesian Progressive Deep Topic Model with Knowledge Informed Textual Data Coarsening Process
21. ICML 2023Generalized Disparate Impact for Configurable Fairness Solutions in ML
22. ICML 2023Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks
23. ICML 2023Harmonic Neural Networks
24. ICML 2023Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
25. ICML 2023Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling
26. ICML 2023MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks
27. ICML 2023NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition
28. ICML 2023Structured Cooperative Learning with Graphical Model Priors
29. ICML 2023Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data
30. NeurIPS 2023ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
31. NeurIPS 2023Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs
32. NeurIPS 2023Gradient Informed Proximal Policy Optimization
33. NeurIPS 2023H-InDex: Visual Reinforcement Learning with Hand-Informed Representations for Dexterous Manipulation
34. NeurIPS 2023High-dimensional Asymptotics of Denoising Autoencoders
35. NeurIPS 2023Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks
36. NeurIPS 2023Inferring Hybrid Neural Fluid Fields from Videos
37. NeurIPS 2023InsActor: Instruction-driven Physics-based Characters
38. NeurIPS 2023Lie Point Symmetry and Physics-Informed Networks
39. NeurIPS 2023Nonparametric Boundary Geometry in Physics Informed Deep Learning
40. NeurIPS 2023Physics-Driven ML-Based Modelling for Correcting Inverse Estimation
41. NeurIPS 2023Physics-Informed Bayesian Optimization of Variational Quantum Circuits
42. NeurIPS 2023PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification
43. NeurIPS 2023Proximity-Informed Calibration for Deep Neural Networks
44. NeurIPS 2023ResoNet: Noise-Trained Physics-Informed MRI Off-Resonance Correction
45. NeurIPS 2023Separable Physics-Informed Neural Networks
46. NeurIPS 2023Solving Inverse Physics Problems with Score Matching
47. NeurIPS 2023Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints
48. NeurIPS 2023Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
49. NeurIPS 2023SyncTREE: Fast Timing Analysis for Integrated Circuit Design through a Physics-informed Tree-based Graph Neural Network
50. NeurIPS 2023Versatile Energy-Based Probabilistic Models for High Energy Physics

FAQ

How many Physics-Informed ML papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

481 in total: 50 in 2023, 85 in 2024, 147 in 2025, 199 in 2026.

How many Physics-Informed ML papers are at NeurIPS 2026?

NeurIPS 2026 has 54 Physics-Informed ML papers, versus 65 the year before.

How many Physics-Informed ML papers are at ICLR 2026?

ICLR 2026 has 64 Physics-Informed ML papers, versus 38 the year before.

How many Physics-Informed ML papers are at ICML 2026?

ICML 2026 has 51 Physics-Informed ML papers, versus 25 the year before.

How many Physics-Informed ML papers are at CVPR 2026?

CVPR 2026 has 30 Physics-Informed ML papers, versus 19 the year before.

Which conference publishes the most Physics-Informed ML papers?

NeurIPS, with 167 papers from 2023 to 2026.