Latest Research in Machine Learning

Top Research Topics in Machine Learning

• Machine Learning — 20 papers
• Numerical Analysis — 7 papers
• Dynamical Systems — 2 papers
• Digital Holography and Microscopy — 2 papers
• Model Reduction and Neural Networks — 1 papers
• Analysis of PDEs — 1 papers
• Computational Engineering, Finance, and Science — 1 papers
• Topological and Geometric Data Analysis — 1 papers
• Machine Learning — 1 papers
• Artificial Intelligence — 1 papers

Highest-Cited Papers

1. Amortizing Physics-Informed Neural Solvers via Graph Hypernetworks
2. Physical and emergent nonpairwise interactions in oscillator networks: from higher-order phase reduction to coupling design
3. Enhancing Physics-Informed Neural Networks with Domain-aware Fourier Features: Towards Improved Performance and Interpretable Results
4. NOAH: Neural Operator Algebra in Hilbert Space: A New Generation of Deep Learning for PDEs Beyond Neural Operators, with Application to the Navier–Stokes Equations
5. Stochastic Dimension Zeroth-Order Estimator: Stable and Memory-Efficient Training of PINNs
6. Lecture notes on Physics Informed Neural Networks, Neural Operators, and their applications
7. Active Learning with Bayesian Multi-Fidelity Laplace Neural Operators for Oscillatory Parametric PDEs
8. Single-condition neural solvers encode transferable response spaces for parametric differential equations
9. Physics Informed Random Feature Neural Networks for Solving PDEs
10. Direct and Indirect Physics-Informed Neural Networks for Dirichlet Boundary Control of Semilinear Parabolic Equations: A Conditional Error Analysis
11. Posterior Convergence without Force Convergence: Resolution-Stable Sampling for Rough Bayesian Inverse Problems
12. Learning functional components of PDEs from data using neural networks
13. Latent-MoE: Domain-Aware Mixture-of-Experts for PDEs with Multi-Regime Physics
14. Learning to Control PDEs with Differentiable Predictive Control and Time-Integrated Neural Operators
15. The friendship quotient: A measure of local neighborhood persistence in reconstructed phase space
16. A Computational Comparison of Fourier Spectral Differentiation and Spatial Automatic Differentiation in Periodic Physics-Informed Neural Networks
17. Rigorous Error Certification for Neural PDE Solvers: From Empirical Residuals to Solution Guarantees
18. Learning PDE Time-Stepping with Neural Cellular Automata
19. Learning the Geometry of Admissible Hypotheses through Inductive Bias in Training Distributions
20. E8 Resonant Phase‑Space Compression for Predictive Climate Dynamics — E8 Intelligence Research