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Technische Universität München
Technische Universität München

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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"

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

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

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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.

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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

Talk on "Coarse-grained variable and model discovery through latent variables and neural nets"

at the CECAM Workshop on "New frontiers in particle-based multiscale and coarse-grained modeling"

The paper "Physics-constrained, data-driven discovery of coarse-grained dynamics" has been accepted for publication.

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The paper "Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography" has been accepted for publication

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