The paper presents structure‑preserving reduced‑order models for two high‑speed aerodynamic test cases: a clamped thin panel at Mach 1.92 (RC‑19) and a cantilever plate at Mach 5.85 (HyMAX). Each model combines a Ritz‑Galerkin reference formulation with a learned component that retains the exact potential, fits remaining forces, and trains only a dissipation term that cannot add energy, ensuring an energy balance for any weights. The learned models achieve extremely low residuals (≤2.2 × 10⁻¹⁵) and accurately reproduce measured pressure ratios, dominant frequencies, and displacement responses, even under chaotic conditions, without relying on time‑resolved data.
By Miguel A. Aguilo
arXiv:2611. 07666v1 Announce Type: cross Abstract: Rayleigh-Taylor instability (RTI) is of fundamental importance in fields such as inertial confinement fusion and supernova explosions, and its evolution is commonly described by potential--flow theory and buoyancy--drag models.
By Chuanxing Wang, Hui Luo, Guohuai Zhu, Hongbo Cai, Shaoping Zhu, Mingxing Luo
arXiv:2610.27321v1 Announce Type: cross
Abstract: Gradient descent with momentum can be viewed as a particle moving with friction in the loss landscape. In deep learning the learning rate is usually...
By Alexander Itin
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
arXiv:2606. 09929v1 Announce Type: cross Abstract: Physical reservoir computing harnesses nonlinear mechanical dynamics but, by convention, freezes the substrate and trains only a linear readout, presuming the substrate is not usefully trainable.
By Caleb Munigety
arXiv:2607. 23667v1 Announce Type: cross Abstract: A flow surrogate validated on a simple regime is often taken as evidence that the approach will carry to a richer one.
By Georg Winkler, Martin Stoll
arXiv:2603. 17057v2 Announce Type: replace-cross Abstract: Active multi-fidelity surrogate modeling is developed for multi-condition airfoil shape optimization to reduce high-fidelity CFD cost while retaining RANS-consistent aerodynamic metrics.
By Isaac Robledo, Alberto Vilari\~no, Arnau Mir\'o, Oriol Lehmkuhl, Carlos Sanmiguel Vila, Rodrigo Castellanos
arXiv:2607. 20171v3 Announce Type: replace-cross Abstract: A second order finite volume scheme rests on two local quantities: a gradient reconstructed in each cell, and a limiter which scales it down where the reconstruction would overshoot.
By Denis Gueyffier (ONERA -- Institut Polytechnique de Paris)
arXiv:2609.38195v1 Announce Type: new
Abstract: Operators for interfacial problems are trained on reference solutions produced by the solver they are intended to replace. This work develops a data-fr...
By Muhammad Akbar Khan
The study investigates the delayed transition from memorization to generalization—known as grokking—in two‑hidden‑layer MLPs trained on modular arithmetic. By exploring 384 hyperparameter configurations, the authors derive a power‑law scaling relation for the onset time of generalization, showing that data complexity dominates over model capacity. A clear phase boundary at weight decay around 1.0 separates grokking from non‑grokking regimes, and weight norm trajectories indicate implicit regularization during the transition.
By Anish Kataria
arXiv:2607. 20235v1 Announce Type: cross Abstract: Classical regularization removes the binary-collision singularity from the Kepler problem, but its value as a representation for learned Hamiltonian dynamics has not been systematically isolated.
By Abhishek Shankar
The paper introduces a correction framework that grounds a CFD-trained deep learning surrogate model for aerospace aerodynamics using wind‑tunnel pressure‑sensor (PSP) data. By training a correction network on spatially registered PSP measurements at two Mach numbers, the authors adjust the surrogate’s predictions without retraining its core parameters, achieving improved agreement with experimental pressure distributions—especially at the wing suction peak and shock location. The grounded surrogate matches measurements within 2.3–2.7% of the Cp range on unseen angles of attack and outperforms simple interpolation between measured states.
By Nitin Nagesh Kulkarni, Dheeraj Vemula, Yin Yu, Peter Lyu, Juan J. Alonso