In many optimization problems, there is a risk to local optimization. Deep learning systems are not yet appropriate for addressing those problems.

For centuries, mathematicians have tried to prove that Euler’s fluid equations can produce nonsensical answers. A new approach to machine learning has researchers betting that “blowup” is near.

In summary, this work introduces Porous-DeepONet, a deep learning framework designed to learn solution operators for parameterized PDEs in porous media, with a focus on reaction-transport equations.

Researchers at the University of Tokyo developed ADOPT, a novel optimization algorithm that overcomes convergence issues in adaptive gradient methods, promising more reliable and efficient ...

Their deep neural network has a billion parameters that need to be tuned in a way that produces the desired output. This process of optimization—or learning—occurs by iteration.

The method uses deep learning and pathwise forward–backward stochastic differential equations to solve boundary value problems (eg, for barrier options) by adding nodes to the computational graph to ...

Optimization problems can be tricky, but they make the world work better. These kinds of questions, which strive for the best way of doing something, are absolutely everywhere. Your phone’s GPS ...

ELEC_ENG 395, 495: Optimization Techniques for Machine Learning and Deep Learning This course is not currently offered. Prerequisites A thorough understanding of Linear Algebra and Vector Calculus, ...