Technical papers and research advancing deterministic, solver-accurate physics simulation for real-world engineering systems.
A research paper defining Continuous Physics Reasoning and the minimum criteria for systems that claim to enable it, including the role of foundation models for physics.
Power densities in modern AI accelerators and HPC devices now routinely exceed 1 W/mm². At the same time, 2.5D packages co-integrate high-power accelerator ASICs (e.g., NVIDIA, AMD) with 8–16 High Bandwidth Memory (HBM) stacks (e.g., Micron, Samsung, SK Hynix), pushing system-level thermal behavior to fundamental limits.
This research introduces a dynamic reinforcement learning approach that leverages continuous feedback to adapt agent behavior in real time. Results indicate substantial improvements in decision accuracy and environmental responsiveness, particularly in unpredictable or rapidly changing scenarios.
As semiconductor devices push toward higher integration densities and 3D stacking, thermal management has emerged as a critical design bottleneck.
We introduce copresheaf topological neural networks (CTNNs), a powerful unifying framework that encapsulates a wide spectrum of deep learning architectures, designed to operate on structured data, including images, point clouds, graphs, meshes, and topological manifolds.
This research introduces a dynamic reinforcement learning approach that leverages continuous feedback to adapt agent behavior in real time. Results indicate substantial improvements in decision accuracy and environmental responsiveness, particularly in unpredictable or rapidly changing scenarios.