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Radenko Tanasic

Research Intern

Deep Learning for Numerical Integration

Differential equations serve as essential tools for modeling dynamic systems in natural sciences and engineering. The challenge lies in solving these equations efficiently, striking a balance between accuracy and computational resources. In his research, Radenko investigates the transformative potential of deep models in enhancing numerical integrators. Specifically, he explores the synergy between deep learning models and classical numerical solvers, aiming to construct versatile and improved solvers for differential equations with broad applicability.