TRIPODS+X:RES: Collaborative Research: Creating Inference from Machine Learned and Science Based Generative Models

TRIPODS X:RES：协作研究：从机器学习和基于科学的生成模型中创建推理

基本信息

批准号：
1839217
负责人：
Uros Seljak
金额：
$ 39.94万
依托单位：
University of California-Berkeley
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839217&HistoricalAwards=false
关键词：
TRIPODS+X RES Collaborative Research Creating

项目摘要

In many scientific disciplines computational simulations are used to enhance our understanding of physical processes of complex systems. In all such simulations simplifications are required to make the problem tractable, limiting the scope of the questions that can be addressed. Generally, computational simulations of large systems with many interacting components, based on the governing physics, requires complex and time consuming computations. This project will apply deep learning neural networks (NN) with geometric transformations based on the physics of the system to accurately approximate traditional physics-based computational simulations in a highly efficient manner. The increased efficiency imparted by the NN model will facilitate the asking of scientific questions which are currently computationally intractable. While the proposed work will focus on using this method to discover new strain-induced polar phases and phase competition, and to understand the large-scale structure in the universe, the concepts developed in this work can be applied to computational simulations in other scientific disciplines.The proposed work will focus on the development of foundational data science methods and the application of these methods to augment computationally-expensive science-based generative models in a way that is principled and efficient, thereby enabling improved data-driven scientific inference. The work will place specific emphasis on the design of neural network models, which through physically-significant domain architectures can approximate N-body and highly-correlated phenomena with minimal loss of information. The work will develop tools to guide the discovery and experimental synthesis of new strain-induced polar phases and phase competition, which exhibit enhanced electromechanical responses; and it will expand our simulation capabilities and understanding of the large-scale structure in the universe. Ultimately, this work will provide both domain specific advances, as well as a framework for other domain areas to augment computationally intensive, highly-correlated, N-body problems with data-driven models, which respect the physics of the problem and lead to increased computational efficiency.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在许多科学学科中，计算模拟被用来增强我们对复杂系统物理过程的理解。在所有此类模拟中，都需要进行简化以使问题易于处理，从而限制了可以解决的问题的范围。一般来说，基于控制物理的具有许多相互作用组件的大型系统的计算模拟需要复杂且耗时的计算。该项目将应用具有基于系统物理学的几何变换的深度学习神经网络（NN），以高效的方式准确地近似传统的基于物理学的计算模拟。神经网络模型提高的效率将有助于提出目前在计算上难以解决的科学问题。虽然拟议的工作将侧重于使用这种方法来发现新的应变引起的极性相和相竞争，并了解宇宙中的大规模结构，但这项工作中开发的概念可以应用于其他科学学科的计算模拟拟议的工作将侧重于基础数据科学方法的开发以及这些方法的应用，以原则性和高效的方式增强计算成本昂贵的基于科学的生成模型，从而改进数据驱动的科学推理。这项工作将特别强调神经网络模型的设计，该模型通过具有物理意义的域架构可以以最小的信息损失来近似 N 体和高度相关的现象。这项工作将开发工具来指导新应变诱导极性相和相竞争的发现和实验合成，这些相竞争表现出增强的机电响应；它将扩展我们的模拟能力和对宇宙大尺度结构的理解。最终，这项工作将提供特定领域的进步，以及其他领域的框架，以通过数据驱动模型来增强计算密集型、高度相关的 N 体问题，这些模型尊重问题的物理原理并导致增加该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Probabilistic Autoencoder for Type Ia Supernova Spectral Time Series

Ia型超新星光谱时间序列的概率自编码器

DOI：
10.3847/1538-4357/ac7c08
发表时间：
2022-07-15
期刊：
The Astrophysical Journal
影响因子：
0
作者：
G. Stein;U. Seljak;V. Boehm;G. Aldering;P. Antilogus;C. Aragon;S. Bailey;C. Baltay;S. Bongard
通讯作者：
S. Bongard

Marginal unbiased score expansion and application to CMB lensing

边际无偏分数扩展及其在 CMB 透镜中的应用