From the 1 of 4 linked papers with an AI index.
4 papers
Probabilistic Physics-Informed Neural Networks for Estimating Heterogeneous Elastic Properties from Low-Resolution and Noisy Displacement Data
Tatthapong Srikitrungruang, Jaesung Lee
The paper introduces a probabilistic physics-informed neural network (PIE-PINN) that estimates spatially varying elastic properties, such as Young's modulus and Poisson's ratio, fr…
Efficient Bayesian Deep Ensembles via Analytic Predictive Inference
Sina Aghaee Dabaghan Fard, Marie Maros, Jaesung Lee
We introduce an efficient Bayesian deep ensemble method for predictive regression designed to enhance interpretability while maintaining competitive predictive performance and comp…
Bayesian Joint Model of Multi-Sensor and Failure Event Data for Multi-Mode Failure Prediction
Sina Aghaee Dabaghan Fard, Minhee Kim, Akash Deep +1
Modern industrial systems are often subject to multiple failure modes, and their conditions are monitored by multiple sensors, generating multiple time-series signals. Additionally…
Robust Physics-Informed Neural Network Approach for Estimating Heterogeneous Elastic Properties from Noisy Displacement Data
Tatthapong Srikitrungruang, Matthew Lemon, Sina Aghaee Dabaghan Fard +2
Accurately estimating spatially heterogeneous elasticity parameters, particularly Young's modulus and Poisson's ratio, from noisy displacement measurements remains significantly ch…