Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism by Levi McClenny

Accelerating high-fidelity combustion simulations with classification algorithms by Wai Tong Chung

Физически-информированные нейронные сети (PINN) [Машинное обучение с учетом физики]

SZALONA KOŃCÓWKA, YAMAL W OSTATNIEJ SEKUNDZIE! NEWCASTLE - FC BARCELONA, SKRÓT MECZU

Ultrafast Dynamics and Light-Assisted Photoinduced Polaron Transport in Nanophotonic Architectures

Learning Physics-guided Neural Networks with Competing Physics Loss: Solving Eigenvalue Problems

Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Nets, Rackauckas

FESTIWAL BRAMEK, PARADA POMYŁEK! KOSZMAR BRAMKARZA SPURS! ATLETICO – TOTTENHAM, SKRÓT MECZU

Physics-Informed Neural Networks (PINNs) - An Introduction - Ben Moseley | Jousef Murad

Simulation of pulsed DNP in the steady state | Shebha Anandhi Jegadeesan

ISGC 260211 talk by Dr. Marius Oancea - University of Vienna

Discovery of Physics and Characterization of Microstructure with Bayesian Hidden Physics Models

Self-Adaptive PINNs || MorphNet: Structure Learning of Deep Networks || May 21, 2021.

Model Reduction for the Material Point Method on Nonlinear Manifolds Using DL by Peter Yichen Chen

Neural operator: A new paradigm for learning PDEs by Animashree Anandkumar

Мы заперты в Солнечной системе: почему человечество никогда не долетит до звёзд

International Zoom Inverse Problems Seminar, Feb 12, 2026, Pierre Millien (Institut Langevin)

Talk on "What Kinds of Functions Do Neural Networks Learn? Low-Norm vs. Flat Solutions"

DeCAF seminars 18-02-2026: Searching for ALP Dark Matter across the spectrum

ETH Zürich DLSC: Physics-Informed Neural Networks - Applications

Graph-Informed Neural Networks by Søren Taverniers