Differentiable Framework

Differentiable frameworks leverage the power of gradient-based optimization to solve complex problems traditionally tackled with non-differentiable methods. Current research focuses on extending this approach to diverse areas, including robotics (trajectory optimization, contact modeling), neuroscience (multi-scale brain modeling), and machine learning (neural network training, combinatorial optimization, contrastive learning). This allows for efficient and often improved solutions in these fields, bridging the gap between differentiable and non-differentiable techniques and enabling end-to-end learning in previously intractable scenarios. The resulting advancements have significant implications for various scientific disciplines and practical applications, from improving robotic dexterity to accelerating drug discovery.