Physics informed WNO
Deep neural operators are recognized as an effective tool for learning solution operators of
complex partial differential equations (PDEs). As compared to laborious analytical and …
complex partial differential equations (PDEs). As compared to laborious analytical and …
Multi-fidelity wavelet neural operator with application to uncertainty quantification
Operator learning frameworks, because of their ability to learn nonlinear maps between two
infinite dimensional functional spaces and utilization of neural networks in doing so, have …
infinite dimensional functional spaces and utilization of neural networks in doing so, have …
A foundational neural operator that continuously learns without forgetting
T Tripura, S Chakraborty - arXiv preprint arXiv:2310.18885, 2023 - arxiv.org
Machine learning has witnessed substantial growth, leading to the development of
advanced artificial intelligence models crafted to address a wide range of real-world …
advanced artificial intelligence models crafted to address a wide range of real-world …
SWENet: a physics-informed deep neural network (PINN) for shear wave elastography
Shear wave elastography (SWE) enables the measurement of elastic properties of soft
materials in a non-invasive manner and finds broad applications in various disciplines. The …
materials in a non-invasive manner and finds broad applications in various disciplines. The …
[HTML][HTML] A review of brain injury at multiple time scales and its clinicopathological correlation through in silico modeling
Understanding the correlation between pathological changes and the type of brain injury is
pivotal in mitigating the damage and planning reliable and improved treatment strategies …
pivotal in mitigating the damage and planning reliable and improved treatment strategies …
Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data
Fault detection and isolation in complex systems are critical to ensure reliable and efficient
operation. However, traditional fault detection methods often struggle with issues such as …
operation. However, traditional fault detection methods often struggle with issues such as …
Size lowerbounds for deep operator networks
A Mukherjee, A Roy - arXiv preprint arXiv:2308.06338, 2023 - arxiv.org
Deep Operator Networks are an increasingly popular paradigm for solving regression in
infinite dimensions and hence solve families of PDEs in one shot. In this work, we aim to …
infinite dimensions and hence solve families of PDEs in one shot. In this work, we aim to …
Neuroscience inspired neural operator for partilial differential equations
S Garg, S Chakraborty - Journal of Computational Physics, 2024 - Elsevier
We propose, in this paper, a Variable Spiking Wavelet Neural Operator (VS-WNO), which
aims to bridge the gap between theoretical and practical implementation of Artificial …
aims to bridge the gap between theoretical and practical implementation of Artificial …
[PDF][PDF] Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning.
Y Mei, J Deng, D Zhao, C Xiao, T Wang… - … in Engineering & …, 2024 - cdn.techscience.cn
Elastography is a non-invasive medical imaging technique to map the spatial variation of
elastic properties of soft tissues. The quality of reconstruction results in elastography is …
elastic properties of soft tissues. The quality of reconstruction results in elastography is …
Neuroscience inspired scientific machine learning (Part-2): Variable spiking wavelet neural operator
S Garg, S Chakraborty - arXiv preprint arXiv:2311.14710, 2023 - arxiv.org
We propose, in this paper, a Variable Spiking Wavelet Neural Operator (VS-WNO), which
aims to bridge the gap between theoretical and practical implementation of Artificial …
aims to bridge the gap between theoretical and practical implementation of Artificial …