Evidence map›Paper›PMID 37758757›Full record

ArticleScientific reports2023

Predictive scale-bridging simulations through active learning.

Satish Karra, Mohamed Mehana, Nicholas Lubbers, Yu Chen, Abdourahmane Diaw, Javier E Santos, Aleksandra Pachalieva, Robert S Pavel, Jeffrey R Haack, Michael McKerns and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Satish Karra *Energy and Natural Resources Security Group, Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Mohamed Mehana *Energy and Natural Resources Security Group, Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. mzm@lanl.gov.
Nicholas LubbersInformation Sciences Group, Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Yu ChenDepartment of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzen, 518055, China.
Abdourahmane DiawBurning Plasma Foundations Section, Fusion Energy Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA.
Javier E SantosEnergy and Natural Resources Security Group, Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Aleksandra PachalievaEnergy and Natural Resources Security Group, Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Robert S PavelApplied Computer Science Group, Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Jeffrey R HaackComputational Physics and Methods, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Michael McKernsComputational Physics and Methods, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Christoph JunghansApplied Computer Science Group, Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Qinjun KangEnergy and Natural Resources Security Group, Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Daniel LivescuComputational Physics and Methods, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Timothy C GermannPhysics and Chemistry of Materials Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Hari S ViswanathanEnergy and Natural Resources Security Group, Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models.

Identifiers

PMID37758757
PMCPMC10533863

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.