21 papers
Out-of-Distribution Detection in Molecular Complexes via Diffusion Models for Irregular Graphs
David Graber, Victor Armegioiu, Rebecca Buller +1
Predictive machine learning models generally excel on in-distribution data, but their performance degrades on out-of-distribution (OOD) inputs. Reliable deployment therefore requir…
Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows
Janne Perini, Rafael Bischof, Moab Arar +4
Designing urban spaces that provide pedestrian wind comfort and safety requires time-resolved Computational Fluid Dynamics (CFD) simulations, but their current computational cost m…
Projected Inverse Iteration: An Eigenvalue Approach to Ground-State Computation with Neural Quantum States
Hang Zhang, Victor Armegioiu, Juan Carrasquilla +4
Deep learning offers a powerful approach to quantum many-body problems via neural network wavefunctions, but their optimization remains a severe bottleneck. Existing optimization m…
Learning, Solving and Optimizing PDEs with TensorGalerkin: an efficient high-performance Galerkin assembly algorithm
Shizheng Wen, Mingyuan Chi, Tianwei Yu +5
We present a unified algorithmic framework for the numerical solution, constrained optimization, and physics-informed learning of PDEs with a variational structure. Our framework i…
Neuro-Symbolic AI for Analytical Solutions of Differential Equations
Orestis Oikonomou, Levi Lingsch, Dana Grund +2
Analytical solutions to differential equations offer exact, interpretable insight but are rarely available because discovering them requires expert intuition or exhaustive search o…
Imposing Boundary Conditions on Neural Operators via Learned Function Extensions
Sepehr Mousavi, Siddhartha Mishra, Laura De Lorenzis
Neural operators have emerged as powerful surrogates for the solution of partial differential equations (PDEs), yet their ability to handle general, highly variable boundary condit…