"While deriving the analytic solution can be a tedious or, in some cases, an impossible task, a physics-informed neural network (PINN) produces the solution directly from the differential equation."
@focusthefuture expands in a new explainer. https://t.co/vgBxtBPQWm
In his new exploration of physics-informed neural networks (PINN), @focusthefuture explains how we can solve differential equations directly with neural networks, and provides a full code implementation. https://t.co/vgBxtBPQWm
https://t.co/OVHYY8jxUE
Determining optimal control settings for an industrial process can be challenging. This article addresses the problem using genetic optimization.
#AI#MachineLearning
inverse Physics-Informed Neural Networks
~ training neural networks to solve the many relationships in physics, biology, chemistry, economics, engineering, etc., that are defined by differential equations
https://t.co/pPQwTRroum