3 citations · 5 across the 5 of their papers we have counts for
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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…
Stabilizing Backpropagation Through Time to Learn Complex Physics
Patrick Schnell, Nils Thuerey
Of all the vector fields surrounding the minima of recurrent learning setups, the gradient field with its exploding and vanishing updates appears a poor choice for optimization, of…
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…