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Found 5 papers, 0 papers with code
Zero-Level-Set Encoder for Neural Distance Fields
Overall, this can help reduce the computational overhead of training and evaluating neural distance fields, as well as enabling the application to difficult shapes.
Wavelet-based Loss for High-frequency Interface Dynamics
As an alternative, we present a new method based on a wavelet loss formulation, which remains transparent in terms of what is optimized.
Accurately Solving Rod Dynamics with Graph Learning
In this contribution, we introduce a novel method to accelerate iterative solvers for rod dynamics with graph networks (GNs) by predicting the initial guesses to reduce the number of iterations.
Frequency-aware Interface Dynamics with Generative Adversarial Networks
We present a new method for reconstructing and refining complex surfaces based on physical simulations.
Accurately Solving Physical Systems with Graph Learning
In this contribution, we introduce a novel method to accelerate iterative solvers for physical systems with graph networks (GNs) by predicting the initial guesses to reduce the number of iterations.
