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Found 6 papers, 0 papers with code
Deep Convolutional Architectures for Extrapolative Forecast in Time-dependent Flow Problems
Physical systems whose dynamics are governed by partial differential equations (PDEs) find applications in numerous fields, from engineering design to weather forecasting.
Energy networks for state estimation with random sensors using sparse labels
Based on this technique we present two models for discrete and continuous prediction in space.
ML-Based Analysis to Identify Speech Features Relevant in Predicting Alzheimer's Disease
Confusion matrices and feature importance graphs have been plotted model-wise to compare the performances of our models.
GrADE: A graph based data-driven solver for time-dependent nonlinear partial differential equations
With recent developments in the field of artificial intelligence and machine learning, the solution of PDEs using neural network has emerged as a domain with huge potential.
Search for a viable nucleus-nucleus potential for heavy-ion nuclear reactions
Similarly, current interaction barrier predictions have also been compared well with a few experimental results available and Bass potential model meant for the interaction barrier predictions.
Nuclear Theory
State estimation with limited sensors -- A deep learning based approach
We illustrate that utilizing sequential data allows for state recovery from only one or two sensors.
