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Found 7 papers, 1 papers with code
Homotopy Relaxation Training Algorithms for Infinite-Width Two-Layer ReLU Neural Networks
In this paper, we present a novel training approach called the Homotopy Relaxation Training Algorithm (HRTA), aimed at accelerating the training process in contrast to traditional methods.
HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
Due to the complex behavior arising from non-uniqueness, symmetry, and bifurcations in the solution space, solving inverse problems of nonlinear differential equations (DEs) with multiple solutions is a challenging task.
Stability Preserving Data-driven Models With Latent Dynamics
In this paper, we introduce a data-driven modeling approach for dynamics problems with latent variables.
A Weight Initialization Based on the Linear Product Structure for Neural Networks
To address this challenge, we study neural networks from a nonlinear computation point of view and propose a novel weight initialization strategy that is based on the linear product structure (LPS) of neural networks.
Projection based model reduction for the immersed boundary method
Fluid-structure interactions are central to many bio-molecular processes, and they impose a great challenge for computational and modeling methods.
Numerical Analysis Numerical Analysis
AN EFFICIENT HOMOTOPY TRAINING ALGORITHM FOR NEURAL NETWORKS
We present a Homotopy Training Algorithm (HTA) to solve optimization problems arising from neural networks.
A machine learning method correlating pulse pressure wave data with pregnancy
Pulse feeling, representing the tactile arterial palpation of the heartbeat, has been widely used in traditional Chinese medicine (TCM) to diagnose various diseases.
