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Technische Universität München

News

Martin Kronthaler presents his Semester Thesis on "Physics Enhanced Neural Networks for the Prediction of Dynamical Systems" on 24.01.2020

New work on "Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems"

More details here Github repository here

Tobias Pielok presents his Semester Thesis on "Bayesian Coarse-Graining with Memory" on November 22nd

Andreas Felderer presents his B.Sc. thesis on "Machine Learning Tools for the Solution of High Dimensional Problems in Random Media"

New work on "A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime"

More details here

Our group will participate in this year's SIAM CSE 2019 conference with 5 papers

More details here

New work on "Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data"

More details here

Talk on "Data-driven, physics-aware, coarse-grained models"

at the Seminar on Numerical Analysis and Applied Mathematics at KU Leuven

The paper "Predictive Collective Variable Discovery with Deep Bayesian Models" has been accepted for publication.

More details here

Keynote talk on "Physics-aware, coarse-grained models for high-dimensional, uncertainty quantification"

at the "Big Data Challenges for Predictive Modeling of Complex Systems" Workshop at the University of Hong Kong

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