1.4k citations · 5.1k across the 32 of their papers we have counts for
6 papers · 1 filter
PINNs-TF2: Fast and User-Friendly Physics-Informed Neural Networks in TensorFlow V2
Reza Akbarian Bafghi, Maziar Raissi
Physics-informed neural networks (PINNs) have gained prominence for their capability to tackle supervised learning tasks that conform to physical laws, notably nonlinear partial di…
A Survey on Physics Informed Reinforcement Learning: Review and Open Problems
Chayan Banerjee, Kien Nguyen, Clinton Fookes +1
The inclusion of physical information in machine learning frameworks has revolutionized many application areas. This involves enhancing the learning process by incorporating physic…
Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics
R. Sharma, W. Grace Guo, M. Raissi +1
Melt pool dynamics in metal additive manufacturing (AM) is critical to process stability, microstructure formation, and final properties of the printed materials. Physics-based sim…
Real Estate Property Valuation using Self-Supervised Vision Transformers
Mahdieh Yazdani, Maziar Raissi
The use of Artificial Intelligence (AI) in the real estate market has been growing in recent years. In this paper, we propose a new method for property valuation that utilizes self…
Temporal Consistency Loss for Physics-Informed Neural Networks
Sukirt Thakur, Maziar Raissi, Harsa Mitra +1
Physics-informed neural networks (PINNs) have been widely used to solve partial differential equations in a forward and inverse manner using deep neural networks. However, training…
Open Problems in Applied Deep Learning
Maziar Raissi
This work formulates the machine learning mechanism as a bi-level optimization problem. The inner level optimization loop entails minimizing a properly chosen loss function evaluat…