arXiv:2606. 01470v1 Announce Type: cross Abstract: Whether physics foundation models can be usefully deployed on laboratory experiments remains an open question for scientific machine learning (ML).
By Payel Mukhopadhyay, Stefan S. Nixon, Romain Watteaux, Michael McCabe, Alberto Bietti, Kyunghyun Cho, Cristiana Diaconu, Irina Espejo Morales, David Fouhey, Siavash Golkar, Tom Hehir, Shirley Ho, Jake Kovalic, Geraud Krawezik, Francois Lanusse, Tanya Marwah, Rudy Morel, Mariel Pettee, Helen Qu, Jeff Shen, Hadi Sotoudeh, Stuart B. Dalziel, Miles Cranmer
Identifying the governing equations of complex dynamical systems remains a fundamental challenge across science and engineering. While early approaches relied on empirical data and heuristics, modern data-driven methods offer greater flexibility and fewer assumptions.
arXiv:2610.28487v1 Announce Type: new
Abstract: We introduce a two-stage data-driven neural framework for discovering dynamically admissible extreme scenarios. Rather than assuming prior knowledge of...
By Angeline Hu, Ava K. Tse, Kaitlyn S. Yasumura, Luciana Sarabia, Malcolm Yeung, Kavin K. Prasanna, Kai Wong, William Hammond, Ioannis G. Keverkidis, Shengkai Li, Trung V. Phan
The paper presents a method to enhance one‑dimensional rotating detonation engine (RDE) models by integrating data assimilation and machine learning. Continuous data assimilation (nudging) aligns the low‑order Koch‑Kutz solver with high‑fidelity temperature data, while a Jacobian‑regularized closure is trained on the recorded correction forces. The resulting corrected model, once the observation term is removed, autonomously reproduces the temperature spectrum and key statistical properties of the full high‑fidelity simulation.
By Ashwin Suriyanarayanan, Romit Maulik
arXiv:2409. 05885v2 Announce Type: replace Abstract: Characterizing nonlinear flame response is critical for predicting thermoacoustic instabilities in propulsion combustors, yet obtaining a comprehensive response map through high-fidelity simulations remains computationally prohibitive.
By Jiawei Wu, Teng Wang, Jiaqi Nan, Wang Han, Lijun Yang, Jingxuan Li
arXiv:2606. 12182v1 Announce Type: new Abstract: Identifying the governing equations of complex dynamical systems remains a fundamental challenge across science and engineering.
By Ana Larra\~naga, Urban Fasel, Steven L. Brunton