{"total":56,"rows":[{"id":"2511.15684","title":"Walrus: A Cross-Domain Foundation Model for Continuum Dynamics","created":"2025-11-19","cited_by_count":36,"area":null},{"id":"2505.24717","title":"PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations","created":"2025-05-30","cited_by_count":32,"area":null},{"id":"2601.01829","title":"RealPDEBench: A Benchmark for Complex Physical Systems with Real-World Data","created":"2026-01-05","cited_by_count":16,"area":null},{"id":"2602.00884","title":"Test-time Generalization for Physics through Neural Operator Splitting","created":"2026-01-31","cited_by_count":10,"area":null},{"id":"2412.13074","title":"Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates","created":"2024-12-17","cited_by_count":8,"area":null},{"id":"2510.23111","title":"Neural Emulator Superiority: When Machine Learning for PDEs Surpasses its Training Data","created":"2025-10-27","cited_by_count":6,"area":null},{"id":"2601.11428","title":"Diagnosing Failure Modes of Neural Operators Across Diverse PDE Families","created":"2026-01-16","cited_by_count":6,"area":null},{"id":"2605.26324","title":"Semigroup Consistency as a Diagnostic for Learned Physics Simulators","created":"2026-05-25","cited_by_count":4,"area":null},{"id":"2601.08404","title":"Out-of-distribution generalization of deep-learning surrogates for 2D PDE-generated dynamics in the small-data regime","created":"2026-01-13","cited_by_count":3,"area":null},{"id":"2605.14546","title":"Discovering Physical Directions in Weight Space: Composing Neural PDE Experts","created":"2026-05-14","cited_by_count":3,"area":null},{"id":"2505.14595","title":"Physics-informed Reduced Order Modeling of Time-dependent PDEs via Differentiable Solvers","created":"2025-05-20","cited_by_count":2,"area":null},{"id":"2505.14717","title":"AneumoBench: A Source-Linked Benchmark for Synthetic-Geometry Transfer in Aneurysm CFD","created":"2025-05-19","cited_by_count":2,"area":null},{"id":"2509.24868","title":"DRIFT-Net: A Spectral--Coupled Neural Operator for PDEs Learning","created":"2025-09-29","cited_by_count":2,"area":null},{"id":"2509.26282","title":"Reframing Generative Models for Physical Systems using Stochastic Interpolants","created":"2025-09-30","cited_by_count":2,"area":null},{"id":"2511.09729","title":"Generalizing PDE Emulation with Equation-Aware Neural Operators","created":"2025-11-12","cited_by_count":2,"area":null},{"id":"2512.17969","title":"Convolutional-neural-operator-based transfer learning for solving PDEs","created":"2025-12-19","cited_by_count":2,"area":null},{"id":"2602.23113","title":"Learning Physical Operators using Neural Operators","created":"2026-02-26","cited_by_count":2,"area":null},{"id":"2603.04430","title":"Flowers: A Warp Drive for Neural PDE Solvers","created":"2026-02-17","cited_by_count":2,"area":null},{"id":"2605.06591","title":"BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation","created":"2026-05-07","cited_by_count":2,"area":null},{"id":"2606.09949","title":"Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training","created":"2026-06-08","cited_by_count":2,"area":null},{"id":"2512.00564","title":"Pre-Generating Multi-Difficulty PDE Data for Few-Shot Neural PDE Solvers","created":"2025-11-29","cited_by_count":1,"area":null},{"id":"2603.25025","title":"System-Anchored Knee Estimation for Low-Cost Context Window Selection in PDE Forecasting","created":"2026-03-26","cited_by_count":1,"area":null},{"id":"2605.29283","title":"Do Physics Foundation Models Learn Generalizable Physics? A Bias-Aware Benchmark Across Physical Regimes and Distribution Shifts","created":"2026-05-28","cited_by_count":1,"area":null},{"id":"2606.17460","title":"Operator Boosting Produces Pareto-Efficient PDE Surrogates","created":"2026-06-16","cited_by_count":1,"area":null},{"id":"2607.29135","title":"HERO: History-Enriched Rollout Training for Long-Horizon Autoregressive Neural Operators","created":"2026-07-31","cited_by_count":1,"area":null},{"id":"2608.04222","title":"TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning","created":"2026-08-04","cited_by_count":1,"area":null},{"id":"2608.07053","title":"Unsupervised Adaptation of PDE Foundation Models","created":"2026-08-07","cited_by_count":1,"area":null},{"id":"2608.23297","title":"ADDA: a Modular Framework for Representing, Simulating and Assimilating Dynamics with End-to-end Differentiability","created":"2026-08-24","cited_by_count":1,"area":null},{"id":"2509.15236","title":"ChannelFlow-Tools: A Configuration-Driven Pipeline for Generating Machine-Learning-Ready Datasets of 3D Obstructed Channel Flows","created":"2025-09-17","cited_by_count":0,"area":null},{"id":"2509.23453","title":"PHASE: Physics-Integrated, Heterogeneity-Aware Surrogates for Scientific Simulations","created":"2025-09-27","cited_by_count":0,"area":null},{"id":"2602.11090","title":"Direct Learning of Calibration-Aware Uncertainty for Neural PDE Surrogates","created":"2026-02-11","cited_by_count":0,"area":null},{"id":"2602.18801","title":"SGNO: Spectral Generator Neural Operators for Stable Long Horizon PDE Rollouts","created":"2026-02-21","cited_by_count":0,"area":null},{"id":"2603.13496","title":"Deep Invertible Autoencoders for Dimensionality Reduction of Dynamical Systems","created":"2026-03-13","cited_by_count":0,"area":null},{"id":"2604.19465","title":"A neural operator framework for data-driven discovery of stability and receptivity in physical systems","created":"2026-04-21","cited_by_count":0,"area":null},{"id":"2605.05488","title":"A Robust In-Context Model for Conservation Laws: Injecting Context into Flux Neural Operators via Recurrent Vision Transformers","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2605.07098","title":"CarCrashNet: A Large-Scale Dataset and Hierarchical Neural Solver for Data-Driven Structural Crash Simulation","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.08285","title":"Exactness Matters for Physical Rule Enforcement","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.15399","title":"Breakeven complexity: A new perspective on neural partial differential equation solvers","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2606.03926","title":"DiffUNet^2: Bidirectional Conditional Diffusion for Probabilistic Scientific Spatiotemporal Modeling","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2606.27895","title":"Mosaic: A Benchmark Suite for Differentiable Physics Solvers","created":"2026-06-26","cited_by_count":0,"area":null},{"id":"2607.20321","title":"Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields","created":"2026-07-22","cited_by_count":0,"area":null},{"id":"2608.14120","title":"From Fixed Grids to Moving Particles:A Transferable Latent Operator for Fluid Dynamics","created":"2026-08-14","cited_by_count":0,"area":null},{"id":"2608.22026","title":"One-Step Evolution for Long-Time Extrapolation: An Error-Bound-Informed and Prior-Guided Neural Residual Framework for Autonomous PDEs","created":"2026-08-22","cited_by_count":0,"area":null},{"id":"2608.24049","title":"Physics-Integrated Operator Learning via Gaussian Splatting Representations","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2609.22585","title":"TWIG: A Time-Causal Wavelet Operator for Autoregressive Forecasting on Irregular Graphs","created":"2026-09-18","cited_by_count":0,"area":null},{"id":"2609.30718","title":"NEMSim: Learning Control-Conditioned Multi-Event Physical Dynamics via Executable Event-Mechanism Priors","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2609.32864","title":"Staying on the Attractor: Supervising Neural Surrogates of 3D Turbulence Where They Leave It","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.38517","title":"Does Text Steer Neural PDE Surrogates? A Controlled Diagnostic with OperatorCLIP","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2609.38916","title":"Warm-starting PDE solvers with any-dimensional machine learning","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.06952","title":"Do Neural PDE Solvers Learn the Right Dynamics?","created":"2026-10-03","cited_by_count":0,"area":null},{"id":"2610.13317","title":"SBNO : Schr\\\"odinger Bridge Neural Operator for the Forward and Inverse Problems under Missing Information","created":"2026-10-09","cited_by_count":0,"area":null},{"id":"2610.13334","title":"PDE-PFN: Prior-Data Fitted Neural PDE Solver","created":"2026-10-09","cited_by_count":0,"area":null},{"id":"2610.14687","title":"One-Way Coupling via Patchwise Adaptive Normalization in Transformer Surrogates","created":"2026-10-10","cited_by_count":0,"area":null},{"id":"2610.15931","title":"Mesh-Free Convolution: Learned Spectral Attenuation and Transport","created":"2026-10-11","cited_by_count":0,"area":null},{"id":"2610.16167","title":"Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction","created":"2026-10-12","cited_by_count":0,"area":null},{"id":"2610.19552","title":"Test-Time Forecasting: Recurrent Depth Enables In-Context Dynamics Prediction","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}