1.4k citations · 5.1k across the 32 of their papers we have counts for
4 papers · 1 filter
Physics-Informed Neural Networks and Extensions
Maziar Raissi, Paris Perdikaris, Nazanin Ahmadi +1
In this paper, we review the new method Physics-Informed Neural Networks (PINNs) that has become the main pillar in scientific machine learning, we present recent practical extensi…
Physics-Informed Machine Learning for Smart Additive Manufacturing
Rahul Sharma, Maziar Raissi, Y. B. Guo
Compared to physics-based computational manufacturing, data-driven models such as machine learning (ML) are alternative approaches to achieve smart manufacturing. However, the data…
MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised Representations
Reza Akbarian Bafghi, Nidhin Harilal, Claire Monteleoni +1
This paper introduces MixDiff, a new self-supervised learning (SSL) pre-training framework that combines real and synthetic images. Unlike traditional SSL methods that predominantl…
Deep LPPLS: Forecasting of temporal critical points in natural, engineering and financial systems
Joshua Nielsen, Didier Sornette, Maziar Raissi
The Log-Periodic Power Law Singularity (LPPLS) model offers a general framework for capturing dynamics and predicting transition points in diverse natural and social systems. In th…