Advancing deep learning through physics-inspired architectures and training methods, creating more efficient and interpretable neural systems.
Our Neural Networks research program focuses on developing novel architectures and training methods that draw inspiration from physical systems. We believe that understanding the fundamental principles of neural computation can lead to more efficient, robust, and interpretable deep learning systems.
By applying insights from physics, mathematics, and computational neuroscience, we're creating neural networks that are not just powerful, but also principled and well-understood.
Designing neural network architectures that incorporate physical principles such as conservation laws, symmetries, and energy minimization.
Developing training algorithms that leverage physical insights to achieve faster convergence and better generalization.
Creating neural networks whose internal representations and decision-making processes are transparent and understandable.
Building neural networks that can adapt and evolve over time, similar to biological neural systems.
Developing learning frameworks that treat neural networks as energy-based models, leading to more stable and principled training.
Studying the temporal dynamics of neural networks to understand how information flows and transforms through the system.
Our physics-inspired neural networks are advancing computer vision by incorporating geometric and physical constraints, leading to more robust and interpretable visual understanding systems.
We're developing neural architectures for language processing that respect linguistic structure and semantic relationships, enabling more accurate and contextually aware language models.
Our neural networks are being applied to scientific problems where physical accuracy and interpretability are crucial, such as climate modeling and materials science.
Physics-inspired neural networks are enabling more stable and efficient robotic control systems that can adapt to changing environments and constraints.
Coming August 2025 — arXiv preprint
A physics-inspired framework linking deep-network inference, energy bounds, and cardinality-cascade pruning.
Coming October 2025 — arXiv preprint
Novel neural network designs that incorporate physical constraints and conservation principles for improved performance and interpretability.
Interested in advancing the state of neural network research? We're looking for researchers, engineers, and students who want to help build the next generation of deep learning systems.
Contact: [email protected]