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Found 12 papers, 7 papers with code
Solving High Frequency and Multi-Scale PDEs with Gaussian Processes
Machine learning based solvers have garnered much attention in physical simulation and scientific computing, with a prominent example, physics-informed neural networks (PINNs).
ChipNeMo: Domain-Adapted LLMs for Chip Design
ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design.
Genetic Programming Based Symbolic Regression for Analytical Solutions to Differential Equations
In this paper, we present a machine learning method for the discovery of analytic solutions to differential equations.
PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning
Deep Convolutional RL agents trained on this environment produce prefix adder circuits that Pareto-dominate existing baselines with up to 16. 0% and 30. 2% lower area for the same delay in the 32b and 64b settings respectively.
AutoIP: A United Framework to Integrate Physics into Gaussian Processes
Physical modeling is critical for many modern science and engineering applications.
Meta-Learning with Adjoint Methods
the initialization, we only need to run the standard ODE solver twice -- one is forward in time that evolves a long trajectory of gradient flow for the sampled task; the other is backward and solves the adjoint ODE.
Guiding Global Placement With Reinforcement Learning
In this work we augment state-of-the-art, force-based global placement solvers with a reinforcement learning agent trained to improve the final detail placed Half Perimeter Wire Length (HPWL).
Physics Informed Deep Kernel Learning
Deep kernel learning is a promising combination of deep neural networks and nonparametric function learning.
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation
To address these issues, we propose Multi-Fidelity High-Order Gaussian Process (MFHoGP) that can capture complex correlations both between the outputs and between the fidelities to enhance solution estimation, and scale to large numbers of outputs.
SDCNet: Video Prediction Using Spatially-Displaced Convolution
We present an approach for high-resolution video frame prediction by conditioning on both past frames and past optical flows.
SDC-Net: Video prediction using spatially-displaced convolution
We present an approach for high-resolution video frame prediction by conditioning on both past frames and past optical flows.
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Video Prediction
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Large Scale Language Modeling: Converging on 40GB of Text in Four Hours
We provide a learning rate schedule that allows our model to converge with a 32k batch size.
