Nikolaus A. Adams

SchlierenID

Coming soon.

Differentiable Computational Fluid Dynamics as a Layer

SIAM Mathematics of Data Science 2022 (MDS22)

Adaptive, Reinforcement Learning-based Model Management for Multifidelity Monte Carlo

Frontiers of Uncertainty Quantification (Front-UQ)

On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods

An overview on the parallels between the development of numerical approaches to the development of machine learning models to make machine learning models for science more efficient. Published at the AI4Science workshop at ICML'22.

Inference Design for the Uncertainty Quantification of Extreme-Scale Simulations

Laboratory for Applied Mathematics, Numerical Software, and Statistics (LANS) Seminar, Argonne National Laboratory

Data-Driven Inference Design for the Bayesian Uncertainty Quantification of the Reactive Shock-Bubble Interaction

8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS22)

Inference Design for the Uncertainty Quantification of Extreme-Scale Fluid Dynamics Simulations

SIAM Conference on Uncertainty Quantification (UQ22)

Inference Design for the Uncertainty Quantification of the Reactive Shock-Bubble Interaction

Origins Data Science Laboratory, Max Planck Society

Sparse Identification of Truncation Errors

Applying sparse regression to the modified differential equation to identify the truncation errors of numerical schemes. Published in JCP'19.

On Data-Driven Intelligent Approaches

Chair of Computational Mathematics and Simulation Science (MCSS) Seminar, Ecole Polytechnique Federale de Lausanne

Towards an Understanding of Finite-Difference Truncation Errors through the Lens of Sparse Regression

Annual Meeting of the Association of Applied Mathematics and Mechanics (GAMM)

Multifidelity and Machine Learning for Turbulent Flows

Crunch Group Seminar, Brown University

A New Hybrid CNN-RNN Architecture for Nonlinear Multidimensional Spatio-Temporal Data

Workshop on Scientific Machine Learning, The Institute for Computational and Experimental Research in Mathematics (ICERM)