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Found 4 papers, 2 papers with code
A Biased Estimator for MinMax Sampling and Distributed Aggregation
MinMax sampling is a technique for downsampling a real-valued vector which minimizes the maximum variance over all vector components.
Neural oscillators for magnetic hysteresis modeling
Hysteresis is a ubiquitous phenomenon in science and engineering; its modeling and identification are crucial for understanding and optimizing the behavior of various systems.
Neural oscillators for generalization of physics-informed machine learning
A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented by partial differential equations (PDEs).
Discovery of sparse hysteresis models for piezoelectric materials
This article presents an approach for modelling hysteresis in piezoelectric materials, that leverages recent advancements in machine learning, particularly in sparse-regression techniques.
