Tag: AI for Science
All the articles with the tag "AI for Science".
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Single-shot prediction of parametric partial differential equations
Published: at 11:13 AM
99.48 🤔Flexi-VAE introduces a variational autoencoder framework for single-shot forecasting of parametric PDEs, using a neural propagator to achieve efficient, accurate long-horizon predictions with significant speedups over sequential models like AE-LSTM, as validated on Burgers' and advection-diffusion equations.
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ASURA-FDPS-ML: Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback
Published: at 11:07 AM 95.02 🤔This paper introduces ASURA-FDPS-ML, a framework that accelerates high-resolution galaxy simulations by using a machine learning surrogate model for supernova feedback in dense regions, achieving a fourfold speedup while maintaining comparable morphological and outflow characteristics to direct simulations, despite some discrepancies in momentum at higher altitudes.
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An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks
Published: at 11:06 AM 88.71 🤔This paper introduces a GPU sparse kernel generator for the Clebsch-Gordon tensor product in O(3)-equivariant deep networks, achieving significant speedups (up to 10x over e3nn and 1.3x-2.0x over cuEquivariance) by leveraging JIT compilation, static analysis, and kernel fusion, particularly enhancing performance in computational chemistry models like Nequip and MACE.
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Nonparametric learning of covariate-based Markov jump processes using RKHS techniques
Published: at 12:18 AM 88.71 🤔本文提出了一种基于再生核希尔伯特空间（RKHS）的非参数化方法，通过频率学和贝叶斯框架建模连续时间马尔可夫链（CTMC）中协变量驱动的非线性转移率，显著提升了个体化状态转移预测的准确性。
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Quantum-Enhanced LLM Efficient Fine Tuning
Published: at 04:28 PM 72.15 🤔本文提出量子张量混合适配（QTHA）方法，通过整合量子神经网络和张量网络，实现LLM的参数高效微调，显著减少参数量并提升性能，为量子增强人工智能奠定基础。