From the 1 of 6 linked papers with an AI index.
1 citations · 1 across the 2 of their papers we have counts for
6 papers
Neural operators solve inverse problems for constitutive model discovery
Moritz Flaschel, Burigede Liu, Ellen Kuhl
The paper introduces two neural‑operator architectures that learn to map full‑field displacement measurements directly to hyperelastic strain‑energy density functions, enabling rap…
A Quantum Spectral Method for Non-Periodic Boundary Value Problems
Eky Febrianto, Yiren Wang, Burigede Liu +2
Quantum computing holds the promise of solving computational mechanics problems in polylogarithmic time, meaning computational time scales as , where i…
A Learning-based Domain Decomposition Method
Rui Wu, Nikola Kovachki, Burigede Liu
Recent developments in mechanical, aerospace, and structural engineering have driven a growing need for efficient ways to model and analyse structures at much larger and more compl…
Towards Quantum Computational Mechanics
Burigede Liu, Michael Ortiz, Fehmi Cirak
The advent of quantum computers, operating on entirely different physical principles and abstractions from those of classical digital computers, sets forth a new computing paradigm…
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
Zongyi Li, Daniel Zhengyu Huang, Burigede Liu +1
Deep learning surrogate models have shown promise in solving partial differential equations (PDEs). Among them, the Fourier neural operator (FNO) achieves good accuracy, and is sig…
Neural Operator: Learning Maps Between Function Spaces
Nikola Kovachki, Zongyi Li, Burigede Liu +4
The classical development of neural networks has primarily focused on learning mappings between finite dimensional Euclidean spaces or finite sets. We propose a generalization of n…