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Found 7 papers, 5 papers with code
PICL: Physics Informed Contrastive Learning for Partial Differential Equations
A combination of physics-informed system evolution and latent-space model output are anchored to input data and used in our distance function.
Physics Informed Token Transformer for Solving Partial Differential Equations
Solving Partial Differential Equations (PDEs) is the core of many fields of science and engineering.
Neural Network Predicts Ion Concentration Profiles under Nanoconfinement
Overall, our deep learning model is a fast, flexible, and accurate surrogate model to predict ion concentration profiles in nanoconfinement.
MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics
Current machine learning techniques often require substantial computational cost for training data generation, and are restricted in scope to the training data flow regime.
AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning
Inspired by the success of data augmentations in computer vision and natural language processing, we developed AugLiChem: the data augmentation library for chemical structures.
Understanding Uncertainty in Bayesian Deep Learning
Neural Linear Models (NLM) are deep Bayesian models that produce predictive uncertainty by learning features from the data and then performing Bayesian linear regression over these features.
Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks
Neural Linear Models (NLM) are deep Bayesian models that produce predictive uncertainties by learning features from the data and then performing Bayesian linear regression over these features.
