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Found 8 papers, 5 papers with code
From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling
Virtual patients and digital patients/twins are two similar concepts gaining increasing attention in health care with goals to accelerate drug development and improve patients' survival, but with their own limitations.
A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training
Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel ``copy/new'' feedback paradigm to help shape the signal generation of the subject toward the optimal distribution; 2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process.
An Expert's Guide to Training Physics-informed Neural Networks
Physics-informed neural networks (PINNs) have been popularized as a deep learning framework that can seamlessly synthesize observational data and partial differential equation (PDE) constraints.
Random Weight Factorization Improves the Training of Continuous Neural Representations
Continuous neural representations have recently emerged as a powerful and flexible alternative to classical discretized representations of signals.
Improved architectures and training algorithms for deep operator networks
In this work we analyze the training dynamics of deep operator networks (DeepONets) through the lens of Neural Tangent Kernel (NTK) theory, and reveal a bias that favors the approximation of functions with larger magnitudes.
Enhancing the trainability and expressivity of deep MLPs with globally orthogonal initialization
Multilayer Perceptrons (MLPs) defines a fundamental model class that forms the backbone of many modern deep learning architectures.
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets
Deep operator networks (DeepONets) are receiving increased attention thanks to their demonstrated capability to approximate nonlinear operators between infinite-dimensional Banach spaces.
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
Physics-informed neural networks (PINNs) are demonstrating remarkable promise in integrating physical models with gappy and noisy observational data, but they still struggle in cases where the target functions to be approximated exhibit high-frequency or multi-scale features.
