arXiv Machine Learning By Max Aehle, Mih\'aly Nov\'ak, Vassil Vassilev, Nicolas R. Gauger, Lukas Heinrich, Michael Kagan, David Lange

Optimization Using Pathwise Algorithmic Derivatives of Electromagnetic Shower Simulations

Read the original on arXiv Machine Learning →

The Flow has not summarised this story yet — read it at arXiv Machine Learning.

arXiv Machine Learning
Jun 4

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters

arXiv:2606. 04165v1 Announce Type: cross Abstract: High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4.

By Cheng Jiang, Sitian Qian, Kevin Pedro, Oz Amram, Huilin Qu, Maggie Voetberg
arXiv AI
Jun 3

Introduction to optimization methods for training SciML models

arXiv:2601. 10222v2 Announce Type: replace-cross Abstract: Optimization is central to both modern machine learning (ML) and scientific machine learning (SciML), yet the structure of the underlying optimization problems differs substantially across these domains.

By Alena Kopani\v{c}\'akov\'a, Elisa Riccietti
arXiv Machine Learning
Jun 29

Mosaic: A Benchmark Suite for Differentiable Physics Solvers

arXiv:2606. 27895v1 Announce Type: cross Abstract: Differentiable partial differential equation (PDE) solvers underpin solver-in-the-loop ML training, gradient-based optimal control, and inverse problems, yet the practical cost of obtaining correct, usable gradients from a given solver on a given problem is largely undocumented.

By Andrin Rehmann, Heiko Zimmermann, Dion H\"afner