Tag: math.AP

#math.AP

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Academic · 1 min

Symbolic--KAN: Kolmogorov-Arnold Networks with Discrete Symbolic Structure for Interpretable Learning

arXiv:2603.23854v1 Announce Type: new Abstract: Symbolic discovery of governing equations is a long-standing goal in scientific machine learning, yet a fundamental trade-off persists between interpretability …

Salah A Faroughi, Farinaz Mostajeran, Amirhossein Arzani, Shirko Faroughi

16 views Mar 26

Academic · 1 min

Deep Hilbert--Galerkin Methods for Infinite-Dimensional PDEs and Optimal Control

arXiv:2603.19463v1 Announce Type: new Abstract: We develop deep learning-based approximation methods for fully nonlinear second-order PDEs on separable Hilbert spaces, such as HJB equations for …

Samuel N. Cohen, Filippo de Feo, Jackson Hebner, Justin Sirignano

17 views Mar 23

Academic · 1 min

Semi-Autonomous Formalization of the Vlasov-Maxwell-Landau Equilibrium

arXiv:2603.15929v1 Announce Type: new Abstract: We present a complete Lean 4 formalization of the equilibrium characterization in the Vlasov-Maxwell-Landau (VML) system, which describes the motion …

Vasily Ilin

8 views Mar 18

Academic · 1 min

A Machine Learning Approach to the Nirenberg Problem

arXiv:2602.12368v1 Announce Type: new Abstract: This work introduces the Nirenberg Neural Network: a numerical approach to the Nirenberg problem of prescribing Gaussian curvature on $S^2$ …

Gianfranco Cort\'es, Maria Esteban-Casadevall, Yueqing Feng, Jonas Henkel, Edward Hirst, Tancredi Schettini Gherardini, Alexander G. Stapleton

12 views Mar 7

#math.AP

Symbolic--KAN: Kolmogorov-Arnold Networks with Discrete Symbolic Structure for Interpretable Learning

Deep Hilbert--Galerkin Methods for Infinite-Dimensional PDEs and Optimal Control

Semi-Autonomous Formalization of the Vlasov-Maxwell-Landau Equilibrium

A Machine Learning Approach to the Nirenberg Problem

JCG, PC

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