The Goldilocks solution to our math crisis is where relatable problems aren’t so simple that there’s no learning but also not so complex and irrelevant that there's none.

A line of engineering research seeks to develop computers that can tackle a class of challenges called combinatorial optimization problems. These are common in real-world applications such as ...

US quantum computing company IonQ and the Oak Ridge National Laboratory report having demonstrated how the power of quantum can support grid operators to meet emerging challenges. The demonstration ...

Annealing processors (APs) are gaining popularity for solving complex optimization problems. Fully-coupled Ising model APs are especially valued for their flexibility, but balancing capacity (number ...

We all know that calculus courses such as 18.01 and 18.02 are univariate and vector calculus, respectively. Modern applications such as machine learning and large-scale optimization require the next ...

The rise of AI, graphic processing, combinatorial optimization and other data-intensive applications has resulted in data-processing bottlenecks, as ever greater amounts of data must be shuttled back ...

Abstract: The present work proposes a methodology to the problem of short-term economic dispatch of radial and meshed power systems by means of nonlinear programming (NLP). The problem posed will be ...

The paper presents a topology optimization methodology for 2D elastodynamic problems using the boundary element method (BEM). The topological derivative is derived based on the variation method and ...

O2O: ODE-based learning to optimize (O2O) is a comprehensive framework integrating the inertial system with Hessian-driven damping (ISHD) for developing optimization methods. We focus on the ...