Sep 12, 2019 · We introduce an approach for imposing physically motivated inductive biases on graph networks to learn interpretable representations and improved zero-shot ...

We introduce an approach for imposing physically motivated inductive biases on graph networks to learn interpretable representations and improved zero-shot ...

An approach for imposing physically motivated inductive biases on graph networks to learn interpretable representations and improved zero-shot ...

Apr 9, 2024 · This research incorporates physically inspired inductive biases into graph neural networks(GNN) to facilitate the learning of interpretable ...

We introduce an approach for imposing physically motivated inductive biases on graph networks to learn interpretable representations and improved zero-shot ...

Nov 1, 2019 · We introduce an approach for imposing physically motivated inductive biases on graph networks to learn interpretable representations and ...

Jun 25, 2020 · This paper combines Graph Networks with symbolic regression and shows that the strong inductive biases of these models can be used to derive accurate symbolic ...

Dec 15, 2019 · Learning Symbolic Physics with Graph Networks #45. Closed. ChrisRackauckas opened this issue on Dec 15, 2019 · 0 comments.

Graph-structured physical mechanisms are ubiquitous in real-world scenarios, thus revealing underneath formulas is of great importance for scientific ...

Jun 6, 2023 · NVIDIA Modulus is a framework for building, training, and fine-tuning deep learning models for physical systems, otherwise known as physics-informed machine ...