arXiv Machine Learning June 18, 2026 By Pongpisit Thanasutives, Naichang Ke, Yoshinobu Kawahara

Data-driven sparse identification of governing PDEs via knockoff filters and multi-criteria trade-offs

Read the original on arXiv Machine Learning →

arXiv:2605. 26631v2 Announce Type: replace-cross Abstract: We propose KO-PDE-IDENT, a data-driven framework for identifying parsimonious partial differential equations (PDEs) with false discovery rate (FDR) control.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 14

LLM-PDESR: Robust PDE Discovery via Subdomain Weighted Residuals and LLM-Guided Symbolic Hypothesis Generation

arXiv:2607. 10546v1 Announce Type: new Abstract: Discovering governing partial differential equations (PDEs) from noisy observational data is a fundamental challenge in scientific machine learning.

By Jinyang Du, Hao Ma, Xiaohu Shi, Bo Yang, Yanchun Liang, Heow Pueh Lee, Chunguo Wu

llms benchmarks

arXiv Machine Learning

Jul 7

Counterfactual Operator Relevance for PDE Discovery: Screening, Pruning, and Identifiability

arXiv:2506. 20181v2 Announce Type: replace Abstract: We study operator relevance in data-driven partial differential equation (PDE) discovery.

By Ronald Katende

efficiency

arXiv Machine Learning

Jul 28

On the post-hoc Evaluation of PDE Discovery: A Multifaceted Challenge of Scientific Advancement

arXiv:2607. 23753v1 Announce Type: new Abstract: Partial differential equation (PDE) discovery aims to identify from data the governing law of a physical system.

By Baptiste Mathevon, Farah Cherfaoui, Amaury Habrard, Marc Sebban

safety

arXiv Machine Learning

Jun 5

PyCC.id: A package for hypothesis-driven equation discovery with structural identifiability

arXiv:2606. 05191v1 Announce Type: new Abstract: Data-driven equation discovery is fundamentally an inverse problem that seeks to infer the governing differential equations of a system directly from time-series measurements.

By Federico J. Gonzalez

arXiv Machine Learning

Jul 1

Joint discovery of governing partial differential equations from multi-source datasets by competitive optimization

arXiv:2606. 30699v1 Announce Type: new Abstract: Discovering governing equations directly from observational data is a key step towards interpretable scientific machine learning.

By Hao Xu, Siyu Lou, Yuntian Chen, Dongxiao Zhang

arXiv Machine Learning

Jul 31

Learning features from Newton's algorithm: a way to accelerate nonlinear parametrized PDE solvers

arXiv:2607. 28036v1 Announce Type: new Abstract: It is well known that Newton's method converges faster when the initial guess is closer to a root of a system of nonlinear equations.

By R\'emy Vallot (CB, Michelin), Florian de Vuyst (BMBI), Thibault Dairay (CB, Michelin), Mathilde Mougeot (CB, ENSIIE, ENS Paris Saclay)

One email a morning, machine-written

Related stories

LLM-PDESR: Robust PDE Discovery via Subdomain Weighted Residuals and LLM-Guided Symbolic Hypothesis Generation

Counterfactual Operator Relevance for PDE Discovery: Screening, Pruning, and Identifiability

On the post-hoc Evaluation of PDE Discovery: A Multifaceted Challenge of Scientific Advancement

PyCC.id: A package for hypothesis-driven equation discovery with structural identifiability

Joint discovery of governing partial differential equations from multi-source datasets by competitive optimization

Learning features from Newton's algorithm: a way to accelerate nonlinear parametrized PDE solvers