2 citations · 4 across the 5 of their papers we have counts for
5 papers
TRACE: A spatiotemporal contact memory graph network simulator for granular dynamics
Changjian Zhou, Negin Yousefpour, Jie Qi +3
Learned graph simulators provide an efficient alternative to high-fidelity solvers for granular dynamics. However, granular motion depends strongly on inter-granular contact histor…
PhysGuard: Fisher-Guided Gradient Projection for Sim-to-Real Neural PDE Surrogates
Changjian Zhou, Junfeng Fang, Negin Yousefpour +4
Neural operator models trained on simulation data often lose accuracy when applied to experimental measurements due to the sim-to-real gap. Standard fine-tuning with limited real d…
Physics-Inspired Deep Learning and Transferable Models for Bridge Scour Prediction
Negin Yousefpour, Bo Wang
This paper introduces scour physics-inspired neural networks (SPINNs), a hybrid physics-data-driven framework for bridge scour prediction using deep learning. SPINNs integrate phys…
Application of Long-Short Term Memory and Convolutional Neural Networks for Real-Time Bridge Scour Prediction
Tahrima Hashem, Negin Yousefpour
Scour around bridge piers is a critical challenge for infrastructures around the world. In the absence of analytical models and due to the complexity of the scour process, it is di…
Towards an AI-based Early Warning System for Bridge Scour
Negin Yousefpour, Oscar Correa
Scour is the number one cause of bridge failure in many parts of the world. Considering the lack of reliability in existing empirical equations for scour depth estimation and the c…