{"total":42,"rows":[{"id":"2309.15325","title":"Neural Operators for Accelerating Scientific Simulations and Design","created":"2023-09-27","cited_by_count":221,"area":null},{"id":"2306.05697","title":"Group Equivariant Fourier Neural Operators for Partial Differential Equations","created":"2023-06-09","cited_by_count":45,"area":null},{"id":"2305.16274","title":"Non-adversarial training of Neural SDEs with signature kernel scores","created":"2023-05-25","cited_by_count":37,"area":null},{"id":"2308.13212","title":"SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases","created":"2023-08-25","cited_by_count":27,"area":null},{"id":"2303.17671","title":"Neural signature kernels as infinite-width-depth-limits of controlled ResNets","created":"2023-03-30","cited_by_count":25,"area":null},{"id":"2306.03303","title":"Global universal approximation of functional input maps on weighted spaces","created":"2023-06-05","cited_by_count":19,"area":null},{"id":"2302.13752","title":"A Brief Survey on the Approximation Theory for Sequence Modelling","created":"2023-02-27","cited_by_count":15,"area":null},{"id":"2410.12000","title":"Parametric model reduction of mean-field and stochastic systems via higher-order action matching","created":"2024-10-15","cited_by_count":9,"area":null},{"id":"2304.00490","title":"Infinite-dimensional reservoir computing","created":"2023-04-02","cited_by_count":7,"area":null},{"id":"2205.11359","title":"Towards Size-Independent Generalization Bounds for Deep Operator Nets","created":"2022-05-23","cited_by_count":6,"area":null},{"id":"2305.14286","title":"Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics","created":"2023-05-23","cited_by_count":6,"area":null},{"id":"2411.03384","title":"Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion","created":"2024-11-05","cited_by_count":6,"area":null},{"id":"2509.10613","title":"pySigLib -- Fast Signature-Based Computations on CPU and GPU","created":"2025-09-12","cited_by_count":6,"area":null},{"id":"2403.14753","title":"Learning with SASQuaTCh: a Novel Variational Quantum Transformer Architecture with Kernel-Based Self-Attention","created":"2024-03-21","cited_by_count":5,"area":null},{"id":"2505.20349","title":"FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation","created":"2025-05-25","cited_by_count":5,"area":null},{"id":"2601.01021","title":"Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations","created":"2026-01-03","cited_by_count":5,"area":null},{"id":"2402.15656","title":"Learning Semilinear Neural Operators : A Unified Recursive Framework For Prediction And Data Assimilation","created":"2024-02-24","cited_by_count":4,"area":null},{"id":"2506.18339","title":"Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamics","created":"2025-06-23","cited_by_count":4,"area":null},{"id":"2307.04110","title":"Learning Space-Time Continuous Neural PDEs from Partially Observed States","created":"2023-07-09","cited_by_count":3,"area":null},{"id":"2410.16091","title":"Neural Quantum Propagators for Driven-Dissipative Quantum Dynamics","created":"2024-10-21","cited_by_count":3,"area":null},{"id":"2302.04052","title":"Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks","created":"2023-02-08","cited_by_count":2,"area":null},{"id":"2310.05169","title":"Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp","created":"2023-10-08","cited_by_count":2,"area":null},{"id":"2406.00368","title":"Learning Spatiotemporal Dynamical Systems from Point Process Observations","created":"2024-06-01","cited_by_count":2,"area":null},{"id":"2507.06967","title":"Noisy PDE Training Requires Bigger PINNs","created":"2025-07-09","cited_by_count":2,"area":null},{"id":"2510.09685","title":"Deep Neural Networks Inspired by Differential Equations","created":"2025-10-09","cited_by_count":2,"area":null},{"id":"2607.00196","title":"TRIE: An Evaluation Framework for Stochastic PDE Surrogates","created":"2026-06-30","cited_by_count":2,"area":null},{"id":"2407.19557","title":"Neural stochastic Volterra equations: learning path-dependent dynamics","created":"2024-07-28","cited_by_count":1,"area":null},{"id":"2502.03670","title":"Chaos into Order: Neural Framework for Expected Value Estimation of Stochastic Partial Differential Equations","created":"2025-02-05","cited_by_count":1,"area":null},{"id":"2505.18511","title":"SPDEBench: An Extensive Benchmark for Learning Stochastic PDEs","created":"2025-05-24","cited_by_count":1,"area":null},{"id":"2602.11794","title":"Latent-Variable Learning of SPDEs via Wiener Chaos","created":"2026-02-12","cited_by_count":1,"area":null},{"id":"2603.08219","title":"Wiener Chaos Expansion based Neural Operator for Singular Stochastic Partial Differential Equations","created":"2026-03-09","cited_by_count":1,"area":null},{"id":"2307.13503","title":"Continuous Time Evidential Distributions for Irregular Time Series","created":"2023-07-25","cited_by_count":0,"area":null},{"id":"2310.02402","title":"On the Parallel Complexity of Multilevel Monte Carlo in Stochastic Gradient Descent","created":"2023-10-03","cited_by_count":0,"area":null},{"id":"2404.02926","title":"Log-PDE Methods for Rough Signature Kernels","created":"2024-04-01","cited_by_count":0,"area":null},{"id":"2404.19673","title":"Neural Controlled Differential Equations with Quantum Hidden Evolutions","created":"2024-04-30","cited_by_count":0,"area":null},{"id":"2407.20741","title":"Improving PINNs By Algebraic Inclusion of Boundary and Initial Conditions","created":"2024-07-30","cited_by_count":0,"area":null},{"id":"2410.12036","title":"Deep Optimal Sensor Placement for Black Box Stochastic Simulations","created":"2024-10-15","cited_by_count":0,"area":null},{"id":"2512.03243","title":"Novelty detection on path space","created":"2025-12-02","cited_by_count":0,"area":null},{"id":"2603.04431","title":"Uncertainty-Calibrated Spatiotemporal Field Diffusion with Sparse Supervision","created":"2026-02-17","cited_by_count":0,"area":null},{"id":"2603.23072","title":"Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations","created":"2026-03-24","cited_by_count":0,"area":null},{"id":"2605.15806","title":"Martingale Neural Operators: Learning Stochastic Marginals via Doob-Meyer Factorization","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2610.18939","title":"Wong--Zakai Convergence and FNO Learning for Stochastic Volterra Equations","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}