Zobrazeno 1 - 10
of 112
pro vyhledávání: '"Moya, Christian"'
In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification (UQ) framework, to obtain confidence prediction intervals with coverage guarantees for Deep Operator Network (DeepONet) regression. Initially, we enhance the
Externí odkaz:
http://arxiv.org/abs/2402.15406
Approximate Thompson sampling with Langevin Monte Carlo broadens its reach from Gaussian posterior sampling to encompass more general smooth posteriors. However, it still encounters scalability issues in high-dimensional problems when demanding high
Externí odkaz:
http://arxiv.org/abs/2401.11665
Autor:
Mollaali, Amirhossein, Sahin, Izzet, Raza, Iqrar, Moya, Christian, Paniagua, Guillermo, Lin, Guang
In the pursuit of accurate experimental and computational data while minimizing effort, there is a constant need for high-fidelity results. However, achieving such results often requires significant computational resources. To address this challenge,
Externí odkaz:
http://arxiv.org/abs/2311.03639
Neural operators have been applied in various scientific fields, such as solving parametric partial differential equations, dynamical systems with control, and inverse problems. However, challenges arise when dealing with input functions that exhibit
Externí odkaz:
http://arxiv.org/abs/2310.18888
We present a new framework for computing fine-scale solutions of multiscale Partial Differential Equations (PDEs) using operator learning tools. Obtaining fine-scale solutions of multiscale PDEs can be challenging, but there are many inexpensive comp
Externí odkaz:
http://arxiv.org/abs/2308.14188
This paper designs surrogate models with uncertainty quantification capabilities to improve the thermal performance of rib-turbulated internal cooling channels effectively. To construct the surrogate, we use the deep operator network (DeepONet) frame
Externí odkaz:
http://arxiv.org/abs/2306.00810
This paper presents NSGA-PINN, a multi-objective optimization framework for effective training of Physics-Informed Neural Networks (PINNs). The proposed framework uses the Non-dominated Sorting Genetic Algorithm (NSGA-II) to enable traditional stocha
Externí odkaz:
http://arxiv.org/abs/2303.02219
This paper designs an Operator Learning framework to approximate the dynamic response of synchronous generators. One can use such a framework to (i) design a neural-based generator model that can interact with a numerical simulator of the rest of the
Externí odkaz:
http://arxiv.org/abs/2301.12538
This paper develops a Deep Graph Operator Network (DeepGraphONet) framework that learns to approximate the dynamics of a complex system (e.g. the power grid or traffic) with an underlying sub-graph structure. We build our DeepGraphONet by fusing the
Externí odkaz:
http://arxiv.org/abs/2209.10622
We propose a Deep Operator Network~(DeepONet) framework to learn the dynamic response of continuous-time nonlinear control systems from data. To this end, we first construct and train a DeepONet that approximates the control system's local solution o
Externí odkaz:
http://arxiv.org/abs/2206.06536