130 citations · 309 across the 10 of their papers we have counts for
10 papers
Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems
Girnar Goyal, Philipp Holl, Sweta Agrawal +1
Solving inverse problems in physics is central to understanding complex systems and advancing technologies in various fields. Iterative optimization algorithms, commonly used to so…
The Unreasonable Effectiveness of Solving Inverse Problems with Neural Networks
Philipp Holl, Nils Thuerey
Finding model parameters from data is an essential task in science and engineering, from weather and climate forecasts to plasma control. Previous works have employed neural networ…
Half-Inverse Gradients for Physical Deep Learning
Patrick Schnell, Philipp Holl, Nils Thuerey
Recent works in deep learning have shown that integrating differentiable physics simulators into the training process can greatly improve the quality of results. Although this comb…
Simulating Liquids with Graph Networks
Jonathan Klimesch, Philipp Holl, Nils Thuerey
Simulating complex dynamics like fluids with traditional simulators is computationally challenging. Deep learning models have been proposed as an efficient alternative, extending o…
Scale-invariant Learning by Physics Inversion
Philipp Holl, Vladlen Koltun, Nils Thuerey
Solving inverse problems, such as parameter estimation and optimal control, is a vital part of science. Many experiments repeatedly collect data and rely on machine learning algori…
Physics-based Deep Learning
N. Thuerey, B. Holzschuh, P. Holl +5
This document is a hands-on, comprehensive guide to deep learning in the realm of physical simulations. Rather than just theory, we emphasize practical application: every concept i…
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers
Kiwon Um, Robert Brand, Yun +3
Finding accurate solutions to partial differential equations (PDEs) is a crucial task in all scientific and engineering disciplines. It has recently been shown that machine learnin…
Learning to Control PDEs with Differentiable Physics
Philipp Holl, Vladlen Koltun, Nils Thuerey
Predicting outcomes and planning interactions with the physical world are long-standing goals for machine learning. A variety of such tasks involves continuous physical systems, wh…
Deep learning based pulse shape discrimination for germanium detectors
P. Holl, L. Hauertmann, B. Majorovits +3
Experiments searching for rare processes like neutrinoless double beta decay heavily rely on the identification of background events to reduce their background level and increase t…
Holography of Wi-Fi radiation
Philipp Holl, Friedemann Reinhard
Wireless data transmission systems such as WiFi or Bluetooth emit coherent light - electromagnetic waves with precisely known amplitude and phase. Propagating in space, this radiat…