Conformalized-deeponet: A distribution-free framework for uncertainty quantification in deep operator networks
In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification
(UQ) framework, to obtain prediction intervals with coverage guarantees for Deep Operator …
(UQ) framework, to obtain prediction intervals with coverage guarantees for Deep Operator …
Deep learning methods for partial differential equations and related parameter identification problems
Recent years have witnessed a growth in mathematics for deep learning—which seeks a
deeper understanding of the concepts of deep learning with mathematics and explores how …
deeper understanding of the concepts of deep learning with mathematics and explores how …
D2no: Efficient handling of heterogeneous input function spaces with distributed deep neural operators
Neural operators have been applied in various scientific fields, such as solving parametric
partial differential equations, dynamical systems with control, and inverse problems …
partial differential equations, dynamical systems with control, and inverse problems …
On approximating the dynamic response of synchronous generators via operator learning: A step towards building deep operator-based power grid simulators
DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning
We propose a novel fine-tuning method to achieve multi-operator learning through training a
distributed neural operator with diverse function data and then zero-shot fine-tuning the …
distributed neural operator with diverse function data and then zero-shot fine-tuning the …
Bayesian, multifidelity operator learning for complex engineering systems–a position paper
Deep learning has significantly improved the state-of-the-art in computer vision and natural
language processing, and holds great potential to design effective tools for predicting and …
language processing, and holds great potential to design effective tools for predicting and …
A physics-guided bi-fidelity fourier-featured operator learning framework for predicting time evolution of drag and lift coefficients
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 …
is a constant need for high-fidelity results. However, achieving such results often requires …