4 papers · 1 filter
Distillation of Foundation Models for Time-dependent PDEs
Daniel Musekamp, Boshra Ariguib, Andrei Manolache +1
Foundation models for time-dependent partial differential equations (PDEs) are trained on large and diverse collections of physical systems and can generalize effectively to new do…
CALM-PDE: Continuous and Adaptive Convolutions for Latent Space Modeling of Time-dependent PDEs
Jan Hagnberger, Daniel Musekamp, Mathias Niepert
Solving time-dependent Partial Differential Equations (PDEs) using a densely discretized spatial domain is a fundamental problem in various scientific and engineering disciplines,…
Active Learning for Neural PDE Solvers
Daniel Musekamp, Marimuthu Kalimuthu, David Holzmüller +2
Solving partial differential equations (PDEs) is a fundamental problem in science and engineering. While neural PDE solvers can be more efficient than established numerical solvers…
Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations
Jan Hagnberger, Marimuthu Kalimuthu, Daniel Musekamp +1
Transformer models are increasingly used for solving Partial Differential Equations (PDEs). Several adaptations have been proposed, all of which suffer from the typical problems of…