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Found 3 papers, 1 papers with code
Physics-informed Neural Network Estimation of Material Properties in Soft Tissue Nonlinear Biomechanical Models
The development of biophysical models for clinical applications is rapidly advancing in the research community, thanks to their predictive nature and their ability to assist the interpretation of clinical data.
Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes
We propose a novel architecture, named Latent Dynamics Network (LDNet), which is able to discover low-dimensional intrinsic dynamics of possibly non-Markovian dynamical systems, thus predicting the time evolution of space-dependent fields in response to external inputs.
Statistical closure modeling for reduced-order models of stationary systems by the ROMES method
Rather than target these two types of errors, this work proposes to construct a statistical model for the state error itself; it achieves this by constructing statistical models for the generalized coordinates characterizing both the in-plane error (i. e., the error in the trial subspace) and a low-dimensional approximation of the out-of-plane error.
Numerical Analysis
