3 days ago · Our objective is to develop innovative process-guided deep learning models to better capture the dynamics in scientific systems and advance the understanding of ...

Jun 12, 2024 · Introduction to Theory-Guided Machine Learning and Its Applications to Multi-Physics Problems in Engineering. 25-27 June 2024. Overview and General ...

4 days ago · This study briefly describes the concept of guided training of deep neural networks (DNNs) to learn physically reasonable solutions.

6 days ago · After almost a year, our review paper on #Physics-Guided #DeepLearning finally appears at @PNASNews https://t.co/rQ1wBoQLvJ ... Learning dynamical systems from ...

Jun 10, 2024 · Physics Informed Deep Learning has been applied to geological sequestration of CO2 using a wide range of machine learning techniques. One important aspect to ...

3 days ago · APL Machine Learning features vibrant and timely research for two communities: researchers who use machine learning (ML) and data-driven approaches for physical ...

6 days ago · Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics.

Jun 4, 2024 · Physics guided machine learning using simplified theories. Physics of Fluids ... A review of hybrid physics guided machine learning techniques with cyber-physical ...

Jun 20, 2024 · Request PDF | Physics-guided neural network for predicting chemical signatures | Achieving high classification accuracy on trace chemical residues in active ...

Jun 12, 2024 · This paper introduces a framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery. This ...