Quantitative predictions of the physical state of the Earth's subsurface are routinely based on numerical solutions of complex coupled partial differential equations together with estimates of the uncertainties in the material parameters. The resulti...
This paper proposes a modeling method for scattered acoustic fields under complex structures based on Physics-informed Neural Networks (PINNs), with particular attention to the acquisition of training sets and the embedding of physical governing equa...
PURPOSE: To develop a physics-guided deep learning (PG-DL) reconstruction strategy based on a signal intensity informed multi-coil (SIIM) encoding operator for highly-accelerated simultaneous multislice (SMS) myocardial perfusion cardiac MRI (CMR).
In recent years, a plethora of methods combining neural networks and partial differential equations have been developed. A widely known example are physics-informed neural networks, which solve problems involving partial differential equations by tra...
Design problems of finding efficient patterns, adaptation of complex molecules to external environments, affinity of molecules to specific targets, dynamic adaptive behavior of chemical systems, reconstruction of 3D structures from diffraction data a...
We propose and demonstrate a new approach for fast and accurate surrogate modelling of urban drainage system hydraulics based on physics-guided machine learning. The surrogates are trained against a limited set of simulation results from a hydrodynam...
Journal of chemical theory and computation
Aug 8, 2022
The traditional approach of computational biology consists of calculating molecule properties by using approximate classical potentials. Interactions between atoms are described by an energy function derived from physical principles or fitted to expe...
Quality risk management is an important task when it pertains to the pharmaceutical industry, as this is directly related to product performance. With the ICH Q9 guidelines, several regulatory bodies have encouraged the pharmaceutical industry to imp...
Proceedings of the National Academy of Sciences of the United States of America
Aug 4, 2022
Using simulations or experiments performed at some set of temperatures to learn about the physics or chemistry at some other arbitrary temperature is a problem of immense practical and theoretical relevance. Here we develop a framework based on stati...
Neural networks : the official journal of the International Neural Network Society
Jul 26, 2022
The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns from large datasets with minimal human intervention. However, it is diffi...