Dynamical systems arising in mechanics exhibit rich geometric structure (symmetries, conservation laws, etc.), posing unique challenges for the design and analysis of computational methods. This workshop focuses on recent advances in structure-preserving numerical algorithms for such systems.
Interactions with other workshops:
Foundations of Data Science and Machine Learning
Potential interactions include dynamical aspects of machine learning (integration of neural ODEs, symplectic methods for adjoint systems, etc.) and structure-preserving machine learning (learning dynamics of mechanical systems with structure, scientific machine learning, etc.).
Foundations of Numerical PDEs
Potential interactions include structure-preserving methods for PDEs (e.g., finite element exterior calculus) and methods for PDEs arising in computational mechanics (Hamiltonian PDEs, Hamilton–Jacobi equations, continuum mechanics of solids and fluids, etc.).
Stochastic Computation
Potential interactions include structure-preserving methods for stochastic differential equations, topics arising in computational statistical mechanics, Hamiltonian Monte Carlo, etc.
Computational Dynamics
Potential interactions related to geometric structure preservation in computational dynamics and dynamical aspects of numerical integrators.
Organizers
University of California
Friedrich-Alexander-Universität
Washington University
Speakers
Semi-plenary speakers
Universitat Jaume I
ICMAT
Invited speakers
Universidad Politécnica de Madrid
University of Tübingen
Friedrich-Alexander-Universität
Norwegian U. of Science & Technology
University of Cambridge
Georgia Institute of Technology
University of Göttingen
The Arctic U. of Norway, U. of Bergen
University of Colorado Boulder
Nanyang Technological U.
University of Cambridge
University of Twente
University of Cambridge
Paderborn University
University of Geneva
Max Planck Institute for Plasma Physics
University of Pisa
Scuola Normale Superiore di Pisa