SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes
Paper with Distinction
Nima Negarandeh*, Carlos Mora*, Ramin Bostanabad
*Equal contribution
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference·Paper·arXiv·Code
Operator Learning with Gaussian Processes
Carlos Mora, Amin Yousefpour, Shirin Hosseinmardi, Houman Owhadi and Ramin Bostanabad
Computer Methods in Applied Mechanics and Engineering·Paper·arXiv·Code
Simultaneous and Meshfree Topology Optimization with Physics-informed Gaussian Processes
Amin Yousefpour, Shirin Hosseinmardi, Carlos Mora and Ramin Bostanabad
Computer Methods in Applied Mechanics and Engineering·Paper·arXiv·Code
A gaussian process framework for solving forward and inverse problems involving nonlinear partial differential equations
Carlos Mora, Amin Yousefpour, Shirin Hosseinmardi and Ramin Bostanabad
Computational Mechanics·Paper·arXiv·Code
GP+: A Python library for kernel-based learning via Gaussian processes
Amin Yousefpour, Zahra Zanjani Foumani, Mehdi Shishehbor, Carlos Mora and Ramin Bostanabad
Advances in Engineering Software·Paper·arXiv·Code
Integrating Kernel Methods and Deep Neural Networks for Solving PDEs
Carlos Mora, Amin Yousefpour, Shirin Hosseinmardi and Ramin Bostanabad
ICLR, Workshop on AI4DifferentialEquations In Science·Paper
Probabilistic neural data fusion for learning from an arbitrary number of multi-fidelity data sets
Carlos Mora*, Jonathan Tammer Eweis-Labolle*, Tyler Johnson, Likith Gadde and Ramin Bostanabad
*Equal contribution
Computer Methods in Applied Mechanics and Engineering·Paper·arXiv·Code
Data-Driven Calibration of Multifidelity Multiscale Fracture Models Via Latent Map Gaussian Process
Shiguang Deng, Carlos Mora, Diran Apelian and Ramin Bostanabad
Journal of Mechanical Design·Paper·arXiv
Multi-Fidelity Reduced-Order Models for Multiscale Damage Analyses With Automatic Calibration
Shiguang Deng, Carlos Mora, Diran Apelian, Ramin Bostanabad
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference·Paper
A Gaussian process framework for operator learning
IMECE
A Gaussian Process Framework for PDE Solving and Operator Learning
CRUNCH Seminar·Video
Operator learning via neural networks with kernel-weighted corrective residuals
EMI/PMC
Probabilistic Neural Data Fusion for Learning from an Arbitrary Number of Multi-fidelity Data Sets
Sandia Machine Learning and Deep Learning Workshop
Data Fusion Under Multiple Uncertainty Sources via Multi-Fidelity Bayesian Networks
17th U.S. National Congress on Computational Mechanics
