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Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp
Physics Informed Neural Networks (PINNs) have been achieving ever newer feats of solving complicated PDEs numerically while offering an attractive trade-off between accuracy and speed of inference.
Realistic Data Augmentation Framework for Enhancing Tabular Reasoning
We observed that our framework could generate human-like tabular inference examples, which could benefit training data augmentation, especially in the scenario with limited supervision.
Towards Size-Independent Generalization Bounds for Deep Operator Nets
In recent times machine learning methods have made significant advances in becoming a useful tool for analyzing physical systems.
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