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Found 9 papers, 2 papers with code
Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning
In order to bridge the gap between microscale stochastic fluid models and continuum-based fluid models for bubble dynamics, we develop a composite neural operator model to unify the analysis of nonlinear bubble dynamics across microscale and macroscale regimes by integrating a many-body dissipative particle dynamics (mDPD) model with a continuum-based Rayleigh-Plesset (RP) model through a novel neural network architecture, which consists of a deep operator network for learning the mean behavior of bubble growth subject to pressure variations and a long short-term memory network for learning the statistical features of correlated fluctuations in microscale bubble dynamics.
An enrichment approach for enhancing the expressivity of neural operators with applications to seismology
The Eikonal equation plays a central role in seismic wave propagation and hypocenter localization, a crucial aspect of efficient earthquake early warning systems.
Physics-Informed Computer Vision: A Review and Perspectives
We present a systematic literature review of formulation and approaches to computer vision tasks guided by physical laws.
Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models
We introduce a new approach for solving forward systems of differential equations using a combination of splitting methods and physics-informed neural networks (PINNs).
A physics-informed variational DeepONet for predicting the crack path in brittle materials
We propose a physics-informed variational formulation of DeepONet (V-DeepONet) for brittle fracture analysis.
Learning functionals via LSTM neural networks for predicting vessel dynamics in extreme sea states
Here, we put forward a new simulation paradigm by training recurrent type neural networks (RNNs) that take as input the stochastic wave elevation at a certain sea state and output the main vessel motions, e. g., pitch, heave and roll.
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines.
Machine Learning of Space-Fractional Differential Equations
We extend this framework to linear space-fractional differential equations.
Deep Multi-fidelity Gaussian Processes
We develop a novel multi-fidelity framework that goes far beyond the classical AR(1) Co-kriging scheme of Kennedy and O'Hagan (2000).
