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

102 papers, page 2 of 3

51. ICML 2026Unbiased and Second-Order-Free Training for High-Dimensional PDEs
52. ICML 2025A Machine Learning Approach to Duality in Statistical Physics
53. ICML 2025Calibrated Physics-Informed Uncertainty Quantification
54. ICML 2025Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel
55. ICML 2025Context-Informed Neural ODEs Unexpectedly Identify Broken Symmetries: Insights from the Poincaré–Hopf Theorem
56. ICML 2025CoPINN: Cognitive Physics-Informed Neural Networks
57. ICML 2025DISCO: learning to DISCover an evolution Operator for multi-physics-agnostic prediction
58. ICML 2025Evolving Minds: Logic-Informed Inference from Temporal Action Patterns
59. ICML 2025Geometry-Informed Neural Networks
60. ICML 2025Holistic Physics Solver: Learning PDEs in a Unified Spectral-Physical Space
61. ICML 2025Learning Single Index Models with Diffusion Priors
62. ICML 2025MIPT: Multilevel Informed Prompt Tuning for Robust Molecular Property Prediction
63. ICML 2025MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation
64. ICML 2025Near-optimal Sketchy Natural Gradients for Physics-Informed Neural Networks
65. ICML 2025NEAR: Neural Electromagnetic Array Response
66. ICML 2025PEINR: A Physics-enhanced Implicit Neural Representation for High-Fidelity Flow Field Reconstruction
67. ICML 2025Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification
68. ICML 2025Physics-Informed Generative Modeling of Wireless Channels
69. ICML 2025Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models
70. ICML 2025Physics-Informed Weakly Supervised Learning For Interatomic Potentials
71. ICML 2025PINNsAgent: Automated PDE Surrogation with Large Language Models
72. ICML 2025Refined generalization analysis of the Deep Ritz Method and Physics-Informed Neural Networks
73. ICML 2025Riemann Tensor Neural Networks: Learning Conservative Systems with Physics-Constrained Networks
74. ICML 2025Structure-informed Risk Minimization for Robust Ensemble Learning
75. ICML 2025Sub-Sequential Physics-Informed Learning with State Space Model
76. ICML 2025Topology-aware Neural Flux Prediction Guided by Physics
77. ICML 2024$S^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
78. ICML 2024A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction
79. ICML 2024Boosting Offline Optimizers with Surrogate Sensitivity
80. ICML 2024Challenges in Training PINNs: A Loss Landscape Perspective
81. ICML 2024Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss
82. ICML 2024Efficient Error Certification for Physics-Informed Neural Networks
83. ICML 2024Learning from Integral Losses in Physics Informed Neural Networks
84. ICML 2024NeuralIndicator: Implicit Surface Reconstruction from Neural Indicator Priors
85. ICML 2024Parameterized Physics-informed Neural Networks for Parameterized PDEs
86. ICML 2024PARCv2: Physics-aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics Modeling
87. ICML 2024Physics and Lie symmetry informed Gaussian processes
88. ICML 2024Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification
89. ICML 2024PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling
90. ICML 2024Pluvial Flood Emulation with Hydraulics-informed Message Passing
91. ICML 2024Stability-Informed Initialization of Neural Ordinary Differential Equations
92. ICML 2023Achieving High Accuracy with PINNs via Energy Natural Gradient Descent
93. ICML 2023Bayesian Progressive Deep Topic Model with Knowledge Informed Textual Data Coarsening Process
94. ICML 2023Generalized Disparate Impact for Configurable Fairness Solutions in ML
95. ICML 2023Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks
96. ICML 2023Harmonic Neural Networks
97. ICML 2023Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
98. ICML 2023Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling
99. ICML 2023MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks
100. ICML 2023NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition

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.