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Found 4 papers, 0 papers with code
Neural Lumped Parameter Differential Equations with Application in Friction-Stir Processing
Lumped parameter methods aim to simplify the evolution of spatially-extended or continuous physical systems to that of a "lumped" element representative of the physical scales of the modeled system.
Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps
That is, a single input can potentially yield many different outputs (whether due to noise, imperfect measurement, or intrinsic stochasticity in the process) and many different inputs can yield the same output (that is, the map is not injective).
Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.
A Topological-Framework to Improve Analysis of Machine Learning Model Performance
As both machine learning models and the datasets on which they are evaluated have grown in size and complexity, the practice of using a few summary statistics to understand model performance has become increasingly problematic.
