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Book ; Online: Magnetohydrodynamics with Physics Informed Neural Operators

Rosofsky, Shawn G. / Huerta, E. A.

2023

Abstract: The modeling of multi-scale and multi-physics complex systems typically involves the use of scientific software that can optimally leverage extreme scale computing. Despite major developments in recent years, these simulations continue to be ... ...

Abstract	The modeling of multi-scale and multi-physics complex systems typically involves the use of scientific software that can optimally leverage extreme scale computing. Despite major developments in recent years, these simulations continue to be computationally intensive and time consuming. Here we explore the use of AI to accelerate the modeling of complex systems at a fraction of the computational cost of classical methods, and present the first application of physics informed neural operators to model 2D incompressible magnetohydrodynamics simulations. Our AI models incorporate tensor Fourier neural operators as their backbone, which we implemented with the TensorLY package. Our results indicate that physics informed neural operators can accurately capture the physics of magnetohydrodynamics simulations that describe laminar flows with Reynolds numbers $Re\leq250$. We also explore the applicability of our AI surrogates for turbulent flows, and discuss a variety of methodologies that may be incorporated in future work to create AI models that provide a computationally efficient and high fidelity description of magnetohydrodynamics simulations for a broad range of Reynolds numbers. The scientific software developed in this project is released with this manuscript. Comment: 32 pages, 24 figures, 1 table. First application of physics informed neural operators to solve magnetohydrodynamics equations, v2: Accepted to Machine Learning: Science and Technology
Keywords	Physics - Computational Physics ; Astrophysics - High Energy Astrophysical Phenomena ; Computer Science - Machine Learning ; 35-04 ; I.2.0 ; J.2
Subject code	501
Publishing date	2023-02-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Physics-inspired spatiotemporal-graph AI ensemble for gravitational wave detection

Tian, Minyang / Huerta, E. A. / Zheng, Huihuo

2023

Abstract: We introduce a novel method for gravitational wave detection that combines: 1) hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational wave signals; and 2) graph neural ... ...

Abstract	We introduce a novel method for gravitational wave detection that combines: 1) hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational wave signals; and 2) graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a detector network. These spatiotemporal-graph AI models are tested for signal detection of gravitational waves emitted by quasi-circular, non-spinning and quasi-circular, spinning, non-precessing binary black hole mergers. For the latter case, we needed a dataset of 1.2 million modeled waveforms to densely sample this signal manifold. Thus, we reduced time-to-solution by training several AI models in the Polaris supercomputer at the Argonne Leadership Supercomputing Facility within 1.7 hours by distributing the training over 256 NVIDIA A100 GPUs, achieving optimal classification performance. This approach also exhibits strong scaling up to 512 NVIDIA A100 GPUs. We then created ensembles of AI models to process data from a three detector network, namely, the advanced LIGO Hanford and Livingston detectors, and the advanced Virgo detector. An ensemble of 2 AI models achieves state-of-the-art performance for signal detection, and reports seven misclassifications per decade of searched data, whereas an ensemble of 4 AI models achieves optimal performance for signal detection with two misclassifications for every decade of searched data. Finally, when we distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, our AI ensemble is capable of processing a decade of gravitational wave data from a three detector network within 3.5 hours. Comment: 12 pages, 5 figures, and 2 tables
Keywords	Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Artificial Intelligence ; General Relativity and Quantum Cosmology ; 68T01 ; 68T35 ; 83C35 ; 83C57
Subject code	006
Publishing date	2023-06-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers

Tian, Minyang / Huerta, E. A. / Zheng, Huihuo

2023

Abstract: We introduce spatiotemporal-graph models that concurrently process data from the twin advanced LIGO detectors and the advanced Virgo detector. We trained these AI classifiers with 2.4 million IMRPhenomXPHM waveforms that describe quasi-circular, spinning, ...

Abstract	We introduce spatiotemporal-graph models that concurrently process data from the twin advanced LIGO detectors and the advanced Virgo detector. We trained these AI classifiers with 2.4 million IMRPhenomXPHM waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers with component masses $m_{\{1,2\}}\in[3M_\odot, 50 M_\odot]$, and individual spins $s^z_{\{1,2\}}\in[-0.9, 0.9]$; and which include the $(\ell, \|m\|) = \{(2, 2), (2, 1), (3, 3), (3, 2), (4, 4)\}$ modes, and mode mixing effects in the $\ell = 3, \|m\| = 2$ harmonics. We trained these AI classifiers within 22 hours using distributed training over 96 NVIDIA V100 GPUs in the Summit supercomputer. We then used transfer learning to create AI predictors that estimate the total mass of potential binary black holes identified by all AI classifiers in the ensemble. We used this ensemble, 3 classifiers for signal detection and 2 total mass predictors, to process a year-long test set in which we injected 300,000 signals. This year-long test set was processed within 5.19 minutes using 1024 NVIDIA A100 GPUs in the Polaris supercomputer (for AI inference) and 128 CPU nodes in the ThetaKNL supercomputer (for post-processing of noise triggers), housed at the Argonne Leadership Computing Facility. These studies indicate that our AI ensemble provides state-of-the-art signal detection accuracy, and reports 2 misclassifications for every year of searched data. This is the first AI ensemble designed to search for and find higher order gravitational wave mode signals. Comment: 4 pages, 2 figures, 1 table; v2: 5 pages, 2 figures, 1 table, accepted to NeurIPS 2023 workshop on Machine Learning and the Physical Sciences
Keywords	Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Artificial Intelligence ; General Relativity and Quantum Cosmology ; 68T01 ; 68T35 ; 83C35 ; 83C57
Subject code	539
Publishing date	2023-09-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

Huerta, E. A. / Zhao, Zhizhen

2021

Abstract: We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the Universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos and cosmic rays cover a wide range of wavelengths and ...

Abstract	We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the Universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos and cosmic rays cover a wide range of wavelengths and time scales. Combining and processing these datasets that vary in volume, speed and dimensionality requires new modes of instrument coordination, funding and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets, have powered the rise of artificial intelligence. This digital revolution now powers a multi-billion dollar industry, with far-reaching implications in technology and society. In this chapter we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in Multi-Messenger Astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017, to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data. Comment: 30 pages, 11 figures. Invited chapter for "Handbook of Gravitational Wave Astronomy"; v2: updated to reflect published version
Keywords	Astrophysics - Instrumentation and Methods for Astrophysics ; Astrophysics - High Energy Astrophysical Phenomena ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning ; General Relativity and Quantum Cosmology ; 83-02 ; 83-04 ; 83-08 ; 85-08 ; 85-10 ; 68T07 ; 68T20 ; I.2 ; I.3 ; I.5 ; J.2
Subject code	006
Publishing date	2021-05-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Applications of physics informed neural operators

Rosofsky, Shawn G. / Majed, Hani Al / Huerta, E. A.

2022

Abstract: We present an end-to-end framework to learn partial differential equations that brings together initial data production, selection of boundary conditions, and the use of physics-informed neural operators to solve partial differential equations that are ... ...

Abstract	We present an end-to-end framework to learn partial differential equations that brings together initial data production, selection of boundary conditions, and the use of physics-informed neural operators to solve partial differential equations that are ubiquitous in the study and modeling of physics phenomena. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our physics-informed neural operators to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled partial differential equations. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide the source code, an interactive website to visualize the predictions of our physics informed neural operators, and a tutorial for their use at the Data and Learning Hub for Science. Comment: 15 pages, 12 figures
Keywords	Physics - Computational Physics ; Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning ; General Relativity and Quantum Cosmology
Subject code	115
Publishing date	2022-03-23
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale.

Chaturvedi, Pranshu / Khan, Asad / Tian, Minyang / Huerta, E A / Zheng, Huihuo

Frontiers in artificial intelligence

2022 Volume 5, Page(s) 828672

Abstract: We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 h. Once fully trained, we optimized these models for ... ...

Abstract	We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 h. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our inference-optimized AI ensemble in the ThetaGPU supercomputer at Argonne Leadership Computer Facility to conduct distributed inference. Using the entire ThetaGPU supercomputer, consisting of 20 nodes each of which has 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, our NVIDIA TensorRT-optimized AI ensemble processed an entire month of advanced LIGO data (including Hanford and Livingston data streams) within 50 s. Our inference-optimized AI ensemble retains the same sensitivity of traditional AI models, namely, it identifies all known binary black hole mergers previously identified in this advanced LIGO dataset and reports no misclassifications, while also providing a 3
Language	English
Publishing date	2022-02-16
Publishing country	Switzerland
Document type	Journal Article
ISSN	2624-8212
ISSN (online)	2624-8212
DOI	10.3389/frai.2022.828672
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Book ; Online: Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers

Khan, Asad / Huerta, E. A. / Zheng, Huihuo

2021

Abstract: We present a deep-learning artificial intelligence model that is capable of learning and forecasting the late-inspiral, merger and ringdown of numerical relativity waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers. ...

Abstract	We present a deep-learning artificial intelligence model that is capable of learning and forecasting the late-inspiral, merger and ringdown of numerical relativity waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers. We used the NRHybSur3dq8 surrogate model to produce train, validation and test sets of $\ell=\|m\|=2$ waveforms that cover the parameter space of binary black hole mergers with mass-ratios $q\leq8$ and individual spins $\|s^z_{\{1,2\}}\| \leq 0.8$. These waveforms cover the time range $t\in[-5000\textrm{M}, 130\textrm{M}]$, where $t=0M$ marks the merger event, defined as the maximum value of the waveform amplitude. We harnessed the ThetaGPU supercomputer at the Argonne Leadership Computing Facility to train our AI model using a training set of 1.5 million waveforms. We used 16 NVIDIA DGX A100 nodes, each consisting of 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, to fully train our model within 3.5 hours. Our findings show that artificial intelligence can accurately forecast the dynamical evolution of numerical relativity waveforms in the time range $t\in[-100\textrm{M}, 130\textrm{M}]$. Sampling a test set of 190,000 waveforms, we find that the average overlap between target and predicted waveforms is $\gtrsim99\%$ over the entire parameter space under consideration. We also combined scientific visualization and accelerated computing to identify what components of our model take in knowledge from the early and late-time waveform evolution to accurately forecast the latter part of numerical relativity waveforms. This work aims to accelerate the creation of scalable, computationally efficient and interpretable artificial intelligence models for gravitational wave astrophysics. Comment: 17 pages, 7 figures, 1 appendix
Keywords	General Relativity and Quantum Cosmology ; Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Artificial Intelligence ; 68T10 ; 85-08 ; 83C35 ; 83C57 ; I.2
Subject code	115
Publishing date	2021-10-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers

Khan, Asad / Huerta, E. A. / Das, Arnav

2020

Abstract: The spin distribution of binary black hole mergers contains key information concerning the formation channels of these objects, and the astrophysical environments where they form, evolve and coalesce. To quantify the suitability of deep learning to ... ...

Abstract	The spin distribution of binary black hole mergers contains key information concerning the formation channels of these objects, and the astrophysical environments where they form, evolve and coalesce. To quantify the suitability of deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers, we introduce a modified version of WaveNet trained with a novel optimization scheme that incorporates general relativistic constraints of the spin properties of astrophysical black holes. The neural network model is trained, validated and tested with 1.5 million $\ell=\|m\|=2$ waveforms generated within the regime of validity of NRHybSur3dq8, i.e., mass-ratios $q\leq8$ and individual black hole spins $ \| s^z_{\{1,\,2\}} \| \leq 0.8$. Using this neural network model, we quantify how accurately we can infer the astrophysical parameters of black hole mergers in the absence of noise. We do this by computing the overlap between waveforms in the testing data set and the corresponding signals whose mass-ratio and individual spins are predicted by our neural network. We find that the convergence of high performance computing and physics-inspired optimization algorithms enable an accurate reconstruction of the mass-ratio and individual spins of binary black hole mergers across the parameter space under consideration. This is a significant step towards an informed utilization of physics-inspired deep learning models to reconstruct the spin distribution of binary black hole mergers in realistic detection scenarios. Comment: 21 pages, 10 figures, 1 appendix, 1 Interactive visualization at https://khanx169.github.io/smr_bbm_v2/interactive_results.html
Keywords	General Relativity and Quantum Cosmology ; Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Artificial Intelligence
Subject code	520
Publishing date	2020-04-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: End-to-end AI Framework for Hyperparameter Optimization, Model Training, and Interpretable Inference for Molecules and Crystals

Park, Hyun / Zhu, Ruijie / Huerta, E. A. / Chaudhuri, Santanu / Tajkhorshid, Emad / Cooper, Donny

2022

Abstract: We introduce an end-to-end computational framework that enables hyperparameter optimization with the DeepHyper library, accelerated training, and interpretable AI inference with a suite of state-of-the-art AI models, including CGCNN, PhysNet, SchNet, ... ...

Abstract	We introduce an end-to-end computational framework that enables hyperparameter optimization with the DeepHyper library, accelerated training, and interpretable AI inference with a suite of state-of-the-art AI models, including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-Net. We use these AI models and the benchmark QM9, hMOF, and MD17 datasets to showcase the prediction of user-specified materials properties in modern computing environments, and to demonstrate translational applications for the modeling of small molecules, crystals and metal organic frameworks with a unified, stand-alone framework. We deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership class computing environments. Comment: 20 pages, 10 images, 6 tables
Keywords	Condensed Matter - Materials Science ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning ; I.2
Subject code	006
Publishing date	2022-12-21
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: GHP-MOFassemble

Park, Hyun / Yan, Xiaoli / Zhu, Ruijie / Huerta, E. A. / Chaudhuri, Santanu / Cooper, Donny / Foster, Ian / Tajkhorshid, Emad

Diffusion modeling, high throughput screening, and molecular dynamics for rational discovery of novel metal-organic frameworks for carbon capture at scale

2023

Abstract: We introduce GHP-MOFassemble, a Generative artificial intelligence (AI), High Performance framework to accelerate the rational design of metal-organic frameworks (MOFs) with high CO2 capacity and synthesizable linkers. Our framework combines a diffusion ... ...

Abstract	We introduce GHP-MOFassemble, a Generative artificial intelligence (AI), High Performance framework to accelerate the rational design of metal-organic frameworks (MOFs) with high CO2 capacity and synthesizable linkers. Our framework combines a diffusion model, a class of generative AI, to generate novel linkers that are assembled with one of three pre-selected nodes into MOFs in a primitive cubic (pcu) topology. The CO2 capacities of these AI-generated MOFs are predicted using a modified version of the crystal graph convolutional neural network model. We then use the LAMMPS code to perform molecular dynamics simulations to relax the AI-generated MOF structures, and identify those that converge to stable structures, and maintain their porous properties throughout the simulations. Among 120,000 pcu MOF candidates generated by the GHP-MOFassemble framework, with three distinct metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer), a total of 102 structures completed molecular dynamics simulations at 1 bar with predicted CO2 capacity higher than 2 mmol/g at 0.1 bar, which corresponds to the top 5% of hMOFs in the hypothetical MOF (hMOF) dataset in the MOFX-DB database. Among these candidates, 18 have change in density lower than 1% during molecular dynamics simulations, indicating their stability. We also found that the top five GHP-MOFassemble's MOF structures have CO2 capacities higher than 96.9% of hMOF structures. This new approach combines generative AI, graph modeling, large-scale molecular dynamics simulations, and extreme scale computing to open up new pathways for the accelerated discovery of novel MOF structures at scale. Comment: 30 pages, 13 figures, 7 tables, 7 appendices
Keywords	Condensed Matter - Materials Science ; Computer Science - Artificial Intelligence ; I.2
Subject code	612 ; 600
Publishing date	2023-06-14
Publishing country	us
Document type	Book ; Online
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