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We present a novel probabilistic approach for generating multi-fidelity data while accounting for errors inherent in both low- and high-fidelity data. In this approach a graph Laplacian constructed from the low-fidelity data is used to define a multi
Externí odkaz:
http://arxiv.org/abs/2409.08211
Autor:
Dasgupta, Agnimitra, Ramaswamy, Harisankar, Murgoitio-Esandi, Javier, Foo, Ken, Li, Runze, Zhou, Qifa, Kennedy, Brendan, Oberai, Assad
We propose a framework to perform Bayesian inference using conditional score-based diffusion models to solve a class of inverse problems in mechanics involving the inference of a specimen's spatially varying material properties from noisy measurement
Externí odkaz:
http://arxiv.org/abs/2406.13154
Generative modeling has drawn much attention in creative and scientific data generation tasks. Score-based Diffusion Models, a type of generative model that iteratively learns to denoise data, have shown state-of-the-art results on tasks such as imag
Externí odkaz:
http://arxiv.org/abs/2405.11738
We propose a novel modular inference approach combining two different generative models -- generative adversarial networks (GAN) and normalizing flows -- to approximate the posterior distribution of physics-based Bayesian inverse problems framed in h
Externí odkaz:
http://arxiv.org/abs/2310.04690
Autor:
Shaddy, Bryan, Ray, Deep, Farguell, Angel, Calaza, Valentina, Mandel, Jan, Haley, James, Hilburn, Kyle, Mallia, Derek V., Kochanski, Adam, Oberai, Assad
Increases in wildfire activity and the resulting impacts have prompted the development of high-resolution wildfire behavior models for forecasting fire spread. Recent progress in using satellites to detect fire locations further provides the opportun
Externí odkaz:
http://arxiv.org/abs/2309.02615
The solution of probabilistic inverse problems for which the corresponding forward problem is constrained by physical principles is challenging. This is especially true if the dimension of the inferred vector is large and the prior information about
Externí odkaz:
http://arxiv.org/abs/2306.04895
Autor:
Pinti, Orazio, Oberai, Assad A.
Low-fidelity data is typically inexpensive to generate but inaccurate. On the other hand, high-fidelity data is accurate but expensive to obtain. Multi-fidelity methods use a small set of high-fidelity data to enhance the accuracy of a large set of l
Externí odkaz:
http://arxiv.org/abs/2304.04862
These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California. They should be accessible to a typical engineering graduate student with a strong background in Applied Mathematics. The main o
Externí odkaz:
http://arxiv.org/abs/2301.00942
In recent years operator networks have emerged as promising deep learning tools for approximating the solution to partial differential equations (PDEs). These networks map input functions that describe material properties, forcing functions and bound
Externí odkaz:
http://arxiv.org/abs/2209.12871
Autor:
Foo, Ken Y., Shaddy, Bryan, Murgoitio-Esandi, Javier, Hepburn, Matt S., Li, Jiayue, Mowla, Alireza, Sanderson, Rowan W., Vahala, Danielle, Amos, Sebastian E., Choi, Yu Suk, Oberai, Assad A., Kennedy, Brendan F.
Publikováno v:
In Computer Methods and Programs in Biomedicine October 2024 255
