Combinatorial optimization problems are often encountered in real-world applications, including logistics, scheduling and ...

Optimization lies at the heart of deep learning, driving neural networks to discover patterns in vast and complex datasets. Early approaches relied on batch gradient descent, which computes exact ...

In recent years, the frequency of weather-related natural disasters—cyclones, torrential rains, floods—has increased as a consequence of global warming. These disasters cause billions of dollars in ...

This course offers an introduction to mathematical nonlinear optimization with applications in data science. The theoretical foundation and the fundamental algorithms for nonlinear optimization are ...

Researchers identify fabrication constraints, reinforcement integration, sensing, and quality control as key factors in ...

Optimization seeks to find the best. It could be to design a process that minimizes capital or maximizes material conversion, to choose operating conditions that maximize throughput or minimize waste, ...

Are you passionate about developing AI-based and quantum-inspired solutions for the next generation of sustainable energy systems? We are now looking for a fully funded Doctoral Researcher to work on ...

A deep reinforcement learning framework optimizes silicon-based photonic crystal fiber modulators, achieving ultra-low ...