Key papers

• HPG-Diff: Hierarchical physics-guided diffusion with differentiable connectivity constraints for topology optimization (2026)
  Jinbo Yang et al.
  4.39
• Reliable mechanistic operator recovery with biologically-informed neural networks: principles for architecture and optimisation design (2026)
  Rebecca M. Crossley et al.
  4.39
• Automatic Differentiation from Scratch: How PyTorch Computes Gradients in Physics-Informed Neural Networks (2026)
  Abdeladhim Tahimi
  4.39
• CayleyR: Solving the TopSpin puzzle via cycle intersection (2026)
  Yuri Baramykov
  4.39
• Reassessing Muon for Matrix Factorization (2026)
  Ali Parviz et al.
  4.39
• Spectral-Informed Neural Networks Outperform Spectral Methods in High-dimensional PDEs (2026)
  Tianchi Yu et al.
  4.39
• Approximation of solutions of parameter-dependent problems by residual neural networks (2026)
  Ana Carpio
  4.39
• An Efficient Newton Algorithm for Nonnegative Matrix Factorization with the Kullback-Leibler Divergence (2026)
  Damien Lesens et al.
  4.39
• A Structure-Adaptive Random Feature Method for High-Dimensional Elliptic PDEs (2026)
  Jiale Linghu et al.
  4.39
• Learned Pairwise Deep Dual-Optimal Inequalities for Stabilizing Column Generation (2026)
  Zhengzhong Ricky You et al.
  3.51
• Multiplicity of Stable Attractors in Disordered Neural Models (2026)
  Raffaele Marino et al.
  3.51
• In-span learning: adapting reduced-order models using their own predictions (2026)
  Amirpasha Hedayat et al.
  2.00
• Operator-Informed Gaussian Processes for Complex Helmholtz Wavefields: From Synthetic Benchmarks to In Vivo Brain Elastography (2026)
  Boyuan Deng et al.
  2.00
• FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers (2026)
  Peiyu Zang et al.
  2.00
• Generalized Neural Operator for Parametric and Boundary-Value Problems (2026)
  Ruoyan Li et al.
  2.00
• Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers (2026)
  Zhangyong Liang et al.
  2.00
• Latent PDE mapping for efficient physics-informed learning across geometries with limited data (2026)
  Ingvild Askim Adde et al.
  2.00
• Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression (2026)
  Hirotaka Sugawara et al.
  2.00
• Explicit Iteration Complexity of Exact Data-Driven Inverse Optimization for Integer Linear Programs (2026)
  Akira Kitaoka
  2.00
• Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows (2026)
  Harish Ramachandran et al.
  2.00
• Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iterates (2026)
  Anjian Li et al.
  2.00
• Beyond Negative-Ridge Endpoints: Mixed-Sign Spectral Regularization via Negative-Shifted Gradient Descent (2026)
  Peng Zhao
  2.00
• Singular value soft-thresholding via the polar decomposition (2026)
  Stephen Becker
  2.00
• Geometry-Conditioned Fourier Neural Operators for Cubic Nonlinear Schrodinger Dynamics on Periodic Domains (2026)
  Emmanuel E. Oguadimma et al.
  1.94
• Geometric Dyson Brownian Motions and the Free Log-Normal Limit for a Non-Square Gaussian Matrix Product (2026)
  Mufan Li et al.
  1.94
• Scalable Gaussian process inference via neural feature maps (2026)
  Anthony Stephenson
  1.89
• DriftXpress: Faster Drifting Models via Projected RKHS Fields (2026)
  Ali Falahati et al.
  1.89
• gp2Scale: A Class of Compactly Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points (2025)
  Marcus M. Noack et al.
  1.61
• AdamNX: An Adam improvement algorithm based on a novel exponential decay mechanism for the second-order moment estimate (2025)
  Meng Zhu et al.
  1.56
• Correlating Cross-Iteration Noise for DP-SGD using Model Curvature (2025)
  Xin Gu et al.
  1.50
• Vector-Valued Reproducing Kernel Banach Spaces for Neural Networks and Operators (2025)
  Sven Dummer et al.
  1.44