Ludger Paehler

Native Compilation of Numba on M1-based Mac

Instructions to build an OpenMP-enabled Numba for development purposes on M1-based Macs.

Scalable Automatic Differentiation of Multiple Parallel Paradigms through Compiler Augmentation

Composable automatic differentiation of parallel programming paradigms at the compiler level. Published at SC'22.

Automatic Differentiation of Fortran with Enzyme

Coming soon.

SchlierenID

Coming soon.

Compiler-based Differentiable Programming for Accelerated Simulations

5th Annual Meeting of the SIAM Texas-Louisiana Section

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.

Reverse-Mode Automatic Differentiation and Optimization of GPU Kernels via Enzyme

Syndication of the publication on the first general automatic differentiation of CUDA & ROCm kernels on the LLVM-level. Published at the Beyond Bayes 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)

Enzyme - High-Performance Automatic Differentiation of LLVM

LLVM-CTH - The Third Workshop on LLVM Compiler and Tools for HPC held in conjunction with ISC-HPC

Automated Batching and Differentiation of Scalar Code in Enzyme

European LLVM Developers' Meeting

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

SIAM Conference on Uncertainty Quantification (UQ22)

An Introduction to Enzyme

Origins Data Science Laboratory, Max Planck Society

How to Use Enzyme to Automatically Differentiate Any LLVM-based Language for CPU, GPU, and More

LLVM Developers' Meeting

Reverse-Mode Automatic Differentiation and Optimization of GPU Kernels via Enzyme

First general automatic differentiation of CUDA & ROCm kernels on the LLVM-level. Published at SC'21.

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.

The Path to Omega-UQ

Nanoshock Journal Club, Technical University of Munich

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)

Multifidelity Uncertainty Quantification

Nanoshock Journal Club, Technical University of Munich