Nils Thuerey, Patrick Schnell
We lifted 5 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| tum-pbs/half-inverse-gradients | canonical | 2 of 2 |
| tum-pbs/PhiFlow | canonical | 0 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| NLO_optimization_framework | Ran | tum-pbs/half-inverse-gradients/Nonlinear_oscillators/NLO_HIG.py code served (permissive licence) · get_code("00b52f87ad855c71") |
| timing_dec | Ran | tum-pbs/half-inverse-gradients/Nonlinear_oscillators/NLO_HIG.py code served (permissive licence) · get_code("8639882900729fb4") |
| construct_orifice3d | Not yet run | tum-pbs/PhiFlow/demos/Top_Opt/geom.py code served (permissive licence) · get_code("c6eaee4d81ccfb3b") |
| to_phi_t | Not yet run | tum-pbs/PhiFlow/demos/Top_Opt/geom.py code served (permissive licence) · get_code("3c7b260d9f6dbef3") |
| to_torch_t | Not yet run | tum-pbs/PhiFlow/demos/Top_Opt/geom.py code served (permissive licence) · get_code("b4c8870cc461fe56") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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 combination represents a more complex optimization task than supervised neural network training, the same gradient-based optimizers are typically employed to minimize the loss function. However, the integrated physics solvers have a profound effect on the gradient flow as manipulating scales in magnitude and direction is an inherent property of many physical processes. Consequently, the gradient flow is often highly unbalanced and creates an environment in which existing gradient-based optimizers perform poorly. In this work, we analyze the characteristics of both physical and neural network optimizations to derive a new method that does not suffer from this phenomenon. Our method is based on a halfinversion of the Jacobian and combines principles of both classical network and physics optimizers to solve the combined optimization task. Compared to state-ofthe-art neural network optimizers, our method converges more quickly and yields better solutions, which we demonstrate on three complex learning problems involving nonlinear oscillators, the Schrödinger equation and the Poisson problem.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2203.10131")
get_code_for_paper("2203.10131")
have("2203.10131")
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