The paper investigates the folded transonic flutter boundary of Isogai’s case A aerofoil, whose elastic axis is positioned half a chord ahead of the leading edge. Using coupled unsteady Euler simulations, a Gaussian‑process surrogate trained via batch active learning, and split conformal prediction, the authors map how the boundary changes as the elastic axis is moved, revealing that the fold exists only when the axis is more than ~0.4 chord ahead and closes between 0.39 and 0.33 chord ahead. Energy budget analyses explain the lower and upper branches of the fold, showing how shock phase and damping of the supersonic surface govern the flutter behavior.
By Ashray Saxena, Sharad Shrivastava
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: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
The paper introduces a world model that learns to predict the evolution of physical systems while respecting key physical principles. By hard‑coding a general structure—generating dynamics from the gradient of a learned energy via a fixed reversible operator and imposing constraints on energy, dissipation, and interventions—the model achieves second‑law compatible dissipation, accurate responses to parameter changes, long‑term stability, and robustness to disturbances. Experiments on an electromagnetic cavity, a particle‑in‑cell grid, and shallow‑water fluid demonstrate that the model can recover accurate constitutive functions, distinguish conserving from dissipating regimes, and transfer learned physics to unseen conditions, outperforming unconstrained models.
By Yufeng Wang, Parivesh Priye, Lu Wei, Haibin Ling
The paper presents a hyperreduction technique that accelerates finite element model updating for discovering neural constitutive models from structural experiments. By combining proper orthogonal decomposition, energy‑conserving sampling, and adjoint sensitivities, the method adapts the reduced trial space and sampled elements during training, enabling a polyconvex neural network to maintain accuracy while drastically reducing computational cost. Experiments on two experimental datasets and a synthetic 3‑D example show training times drop from hours to minutes without sacrificing predictive fidelity.
This paper introduces a polyconvex physics‑augmented neural network (PANN) that uses learned paired directional features to model the anisotropic hyperelastic response of a representative volume element. The network’s energy functions are constructed with convex, componentwise nondecreasing cores (ICNN or ICKAN) and satisfy objectivity, stress‑free reference, two‑dimensional polyconvexity, and prescribed growth conditions. Experiments on multicavity and single‑cavity periodic cells show that the constrained PANN achieves sub‑percent stress and displacement errors, while an unconstrained model loses rank‑one convexity and under‑predicts compression stresses beyond the training data.
By S. Ares de Parga, A. Cornejo, J. A. Hern\'andez, R. Rossi