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At least 523 records · Page 29

An editorial to the Special Issue on “Severe climate Risks”

The history of this Special Issue (https://www.sciencedirect.com/special-issue/10JD7LNJNQ0) indirectly dates back to the early 1990s, when the signature of the United Nations Framework Convention on Climate Change kicked-off an international political process based on one overarching and foundational principle: to avoid “dangerous anthropogenic interference with the climate system” at the global level. More than three decades later, such a principle remains central, though complementary aims made their way through the climate negotiation process, such as the importance of ensuring equity and justice, to give just one example here. Scientific knowledge also considerably progressed and we know more about the range of risks that climate change imposes and will continue to impose to the biosphere and humankind, worldwide and at all territorial levels. It is also clear that societal responses to these risks —“climate adaptation” as we know it— are increasingly happening, but definitely not at the pace of climate risk trends (Berrang-Ford et al., 2021, Erisken et al., 2021, Olazabal and Ruiz De Gopegui, 2021, Magnan et al., 2023a, Reckien et al., 2023, UNEP, 2023). As a result, concerns have emerged over the recent years in both the scientific and policy arenas around the idea that societies may not be able to address all climate risks, and that limits to adaptation and induced residual risks need to be considered more seriously. Such concerns further highlight the continuing importance of the imperative to minimise dangerous anthropogenic interference with the climate system, at any scale. But what does “dangerous interference” mean? How can we decide that we are entering the “dangerous” space, compared to a broader range of climate risks that would qualify as problematic but not necessarily “dangerous”? Who should make such a decision? Which conditions drive risk severity over time, including in the future? And what would be the environmental, economic, social and cultural implications of prioritising some climate risks over others? The Intergovernmental Panel on Climate Change (IPCC) was a pioneer in addressing such questions through the development of the “Key Risks” framing that describes those climate risks having the potential to become dangerous or “severe” over the course of this century (Schellnhuber et al., 2006, Schneider et al., 2007, Oppenheimer et al., 2014, O’Neill et al., 2022). The Fifth and Sixth assessment cycles (AR5 and AR6) went a step further by identifying about 120 Key Risks across regions and sectors, and clustering them into 8 “Representative Key Risks” covering a range of geographical systems (low-lying coasts, and to terrestrial and ocean ecosystems), sectors (critical infrastructure, living standards, human health, food security, and water security) and human dimensions (peace and mobility) (Oppenheimer et al., 2014, O’Neill et al., 2022). This Special Issue was born of the efforts of a range of authors, during the development of the IPCC AR6 main Assessment Report between 2019 and 2022, to characterise Key Risks and Representative Key Risks, and advance knowledge on what shapes “severe climate risks” conceptually as well as in the real-world. The series of papers forming this Special Issue is not intended to cover the topic exhaustively, but rather to give readers an overview through the following narrative: defining “severe climate risks” is highly challenging (Magnan et al., 2023b), but knowledge is expanding on the driving climate hazards (Tebaldi et al., 2023) and their implications on geographical systems, sectors and human dimensions, using here food security (Mirzabaev et al., 2023), human mobility (Gilmore et al., 2024) and peace (Buhaug et al., 2023) as illustrative examples. The overall intention is to support especially decision-makers, whatever the scale or sector considered, in asking severity-driven questions to identify sector- and context-specific “priority” risks from climate change.

54 ENVIRONMENTAL SCIENCES↗

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗

Microfracture-pore structure characterization and water-rock interaction in three lithofacies of the Lower Eagle Ford Formation

The microfracture-pore structures and water-rock interactions of eight samples of three typical lithofacies (three limestone, three wackestone, and two mudstone) from the Lower Eagle Ford Formation have been investigated using an integrated methodology. The methods used were X-ray diffraction (XRD), total organic carbon (TOC) content, pyrolysis, thin-section petrography, scanning electron microscopy (SEM), mercury intrusion porosimetry (MIP), spontaneous water imbibition, and water vapor & nitrogen physisorption. Petrophysical properties such as organic richness, maturity, mineral composition, types and patterns of microfractures and pores, porosity, pore-throat size distribution, pore size distribution, pore connectivity, water adsorption and desorption behavior were determined from these methods, and the results compared among the three lithofacies. For example, the three limestone EAS samples and three wackestone DM samples show a low porosity range from 2.02–3.35%, whereas the two mudstone VRU samples exhibit a much large average porosity of 11.7% by MIP results. Nitrogen physisorption results show that pore size distributions have a good similarity in N2 adsorption volume for the samples with similar lithology. Microfractures are morphologically divided into seven types, with their definitions & pictorial examples presented and possible formation mechanisms proposed. In addition, the water vapor physisorption behavior and their controlling factors are discussed. Here, the water adsorption capacity shows a positive relationship (R 2 = 0.90) with clay content and a negative relationship (R 2 = 0.99) with carbonate content. In general, this integrated study of microfracture-pore structure characterization and water-rock interaction provides some insights of water vapor adsorption behavior in different lithofacies and whether microfractures could be an important pathway for fluid flow.

58 GEOSCIENCES↗

Multidisciplinary Design, Analysis, and Optimization (MDO) for Co-Designed Transmission & Distribution Electric Grid Planning

This paper describes early experiences and example use cases applying multi-disciplinary design analysis and optimization (MDO) to the integrated design of power grids. Adapted from aerospace, MDO enables combining multiple existing tools into a coordinated optimization. Here we use MDO to simultaneously capture integrated transmission-distribution and investment-engineering trade-offs in an automated framework. Example use cases showcase prototype interactions among existing grid models using MDO and hint at the types of integrated analyses enabled by this approach. In addition, we share experiences and thoughts on grid-specific challenges and opportunities to help advance further work in this area.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A conceptual framework for residential energy security in the context of clean energy transitions

Energy security is a crucial aspect of human well-being. As climate change impacts become more evident, countries are constructing equitable, resilient, and sustainable clean energy transition policies to reduce emissions while ensuring energy security. Climate policies globally highlight the importance of national energy security. Furthermore, adequate and affordable access to household energy is also critical to the continued prioritization of climate mitigation. However, past energy security discussions within the broader climate research and policymaking community primarily focused on national-level energy supply as a critical metric of energy security. Less research has explored the potential implications of energy transitions for residential energy security, often focusing on a single dimension of residential energy security. Thus, we conduct a review of journal articles and governmental plans to develop a conceptual framework of residential energy security and facilitate communication among researchers and policymakers. The framework is designed around four foundational pillars, five metrics measuring residential energy security, and seven drivers influencing the metrics. Additionally, we provide policy examples to show how this framework can be applied to inform decision-making. Thus, this paper makes important contributions to the literature by (a) creating a framework to better understand the concept of energy security at the household level for future research and policy-relevant communications, (b) identifying gaps in the current literature, and (c) highlighting instances where aspects of residential energy security are discussed in policies and governmental plans, which help serve as guiding examples for future applications of our framework in the policymaking processes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

The importance of cycle-by-cycle data in performing rapid battery technology development and validation

Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.

25 ENERGY STORAGE↗

Efficient kinetic thermal inverse modeling for organic material decomposition

The prevalent use of organic materials in manufacturing is a fire safety concern, and motivates the need for predictive thermal decomposition models. A critical component of predictive modeling is numerical inference of kinetic parameters from bench scale data. Currently, an active area of computational pyrolysis research focuses on identifying efficient, robust methods for optimization. This paper demonstrates that kinetic parameter calibration problems can successfully be solved using classical gradient-based optimization. We explore calibration examples that exhibit characteristics of concern: high nonlinearity, high dimensionality, complicated schemes, overlapping reactions, noisy data, and poor initial guesses. The examples demonstrate that a simple, non-invasive change to the problem formulation can simultaneously avoid local minima, avoid computation of derivative matrices, achieve a computational efficiency speedup of 10x, and make optimization robust to perturbations of parameter components. Techniques from the mathematical optimization and inverse problem communities are employed. By re-examining gradient-based algorithms, we highlight opportunities to develop kinetic parameter calibration methods that should outperform current methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The quest for efficient oxygenated fuels: Examining interactions between lubricant components and oxygenates

This work empirically evaluated oxygenated blendstock-lubricant compatibility using a novel but simple method. The reactivity or compatibility was evaluated via gas chromatograph equipped with a flame ionization detector (GC-FID) before and after heating for 2h at 150 °C, which would roughly mimic conditions encountered in the combustion chamber of an Spark Ignition (SI) or Compression-Ignition (CI) engine. Five oxygenate blendstocks spanning several functional groups and chain lengths (prenol, isoprenol, butyl acetate, isohexanol, polyoxymethylene dimethyl ethers) were evaluated in a base fuel with nine variations of lubricant components of controlled concentrations, including fully formulated commercial lubricants. Baseline comparative examples were also generated, to decouple the effect of the oxygenate on the mixture, by heating only the base fuel with the lubricant component. Results demonstrate the relative compatibility between noted lubricants and oxygenates in base fuel upon heating. This was illustrated by minimal to no changes in the GC-FID traces. However, reactions of several of the chosen lubricants, especially Molybdenum di (2-ethylhexyl) phosphorodithioate (Molyvan L) and zinc dialkyldithiophosphate (ZDDP), with prenol and polyoxymethylene dimethyl ethers blendstocks showed substantial side reactions compared to corresponding baseline examples. Control experiments without oxygenates but comprising the same lubricant components showed no change in the mixture with heating. We concluded the incompatibility is due to the oxygenate and not the components of the base fuel. This preliminary work is valuable in establishing trends as well as provide useful information when a new component is introduced in the fuel.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The Effect of Air Separations on Fast Pyrolysis Products for Forest Residue Feedstocks

This study investigates the intricate relationship between biomass preprocessing and pyrolysis product yields, employing the air classification technique for the treatment of loblolly pine residues with varying moisture content. A comprehensive exploration of the physicochemical properties of air-classified loblolly pine informs a sophisticated pyrolysis simulation model. Given the complex and multifaceted nature of biomass pyrolysis, operating across diverse temporal and spatial scales, a pyrolysis kinetics-based CFD–DEM simulation method is employed to predict product yields. Results showed that the elevated moisture content amplifies particle adhesiveness, necessitating augmented air velocities for effective separation, thereby influencing the efficiency of the separation process. While carbon and hydrogen contents exhibit relative stability across diverse moisture contents and blower frequencies, the oxygen content undergoes noticeable changes. For example, the oxygen contents were measured as 29.2 and 38.6 wt% in the light fraction of 30% moisture content sample at blower frequencies of 10 and 20 Hz, respectively. An intriguing finding emerges from pyrolysis simulation, indicating that a lower blower frequency in air classification moderately enhances bio-oil yield and significantly improves its quality, particularly in terms of water content. For instance, the water content in the bio-oil was about 1.5% and 10% in the heavy and light fractions, respectively from 10% moisture sample under 15 Hz blower frequency. In summary, a detailed understanding and strategic manipulation of critical material attributes in biomass through efficient fractionation techniques are imperative for advancing fast pyrolysis as a sustainable avenue for renewable energy and chemical production.

09 BIOMASS FUELS↗

The globus compute dataset: An open function-as-a-service dataset from the edge to the cloud

Here we present a unique function-as-a-service (FaaS) dataset capturing the use of the Globus Compute (previously funcX) platform. Globus Compute implements a federated model via which users may deploy endpoints on arbitrary remote computers, from the edge to high performance computing (HPC) cluster, and they may then invoke Python functions on those endpoints via a reliable cloud -hosted service. The dataset covers 31 weeks and includes 2121472 task submissions from 252 users executed on 580 remote computing endpoints. It includes 277386 registered functions. We describe the dataset and various observations, some that are similar to other FaaS datasets, for example, that 74% of tasks run for less than 1 s, and some that are unique to Globus Compute, for example, that endpoints are used in different ways and that the majority of functions are related to scientific computing and machine learning. To the best of our knowledge, this dataset represents the first federated FaaS dataset that includes user workloads, distributed computing endpoints, and analysis of registered function bodies. We expect the dataset to be useful for researching FaaS architectures, workload modeling, container warming, and other distributed computing architectures.

97 MATHEMATICS AND COMPUTING↗

Benchmark study of a new simplified DFN model for shearing of intersecting fractures and faults

It is challenging to quantitatively predict shearing of intersecting fractures/faults because of dynamic frictional contacts accompanied by possible nonlinear rock deformation. To address such challenges, a new conceptual model—the simplified DFN model—was proposed and validated by Hu et al. 46 to use major paths (MPs) to represent complicated DFNs for calculation of shearing. In this work, we conducted a benchmark study for three examples that involve different levels of complexity of intersecting fractures, and correspondingly different numbers of MPs. The codes and software that were used in the benchmark cover a range of continuum, discontinuum and hybrid numerical methods: NMM (LBNL), FLAC3D (LBNL), GBDEM (KIGAM), FRACOD (DynaFrax), and CASRock (CAS). The general consistency between DFN and MP cases as predicted by all the codes/software demonstrates that major paths can be used to simplify the geometry of DFNs in a wide range of software. Disagreement in results made by some software and potential future improvements are discussed. We show that (1) shearing of one or multiple major fractures can be reduced if there are multiple smaller intersecting fractures in that area, which is a useful basis for understanding and controlling induced seismicity and merits further analysis, and (2) the agreement achieved in the benchmark examples provide confidence that the simplified DFN model is a promising conceptual model that can be used for different types of numerical approaches and software for simplifying the analysis of the shearing of intersecting fractures and faults.

58 GEOSCIENCES↗

Dynamic risk assessment for geologic CO 2 sequestration

At a geologic CO 2 sequestration (GCS) site, geologic uncertainty usually leads to large uncertainty in the predictions of properties that influence metrics for leakage risk assessment, such as CO 2 saturations and pressures in potentially leaky wellbores, CO 2 /brine leakage rates, and leakage consequences such as changes in drinking water quality in groundwater aquifers. The large uncertainty in these risk-related system properties and risk metrics can lead to over-conservative risk management decisions to ensure safe operations of GCS sites. The objective of this work is to develop a novel approach based on dynamic risk assessment to effectively reduce the uncertainty in the predicted risk-related system properties and risk metrics. We demonstrate our framework for dynamic risk assessment on two case studies: a 3D synthetic example and a synthetic field example based on the Rock Springs Uplift (RSU) storage site in Wyoming, USA. Results show that the U.S. National Risk Assessment Partnership’s Open Source Integrated Assessment Model (NRAP-Open-IAM) coupled with a conformance evaluation can be used to effectively quantify and reduce the uncertainty in the predictions of risk-related system properties and risk metrics in GCS.

58 GEOSCIENCES↗

Microbubble dynamics and heat transfer in boiling droplets

Dissipating large heat fluxes from a surface is critically important in numerous industrial and natural applications. Boiling based spray cooling and surface texturing are two of the most promising methods being investigated to address this problem. Although our understanding on these topics has significantly improved over past decades, critical gaps remain in the knowledgebase stymieing the realization of their full potential. As an example, while bubble growth in pool boiling have been investigated in detail, comparatively little is known about how the bubbles evolve inside boiling drops. In the present work, we have investigated for the first time, the microbubble dynamics inside water droplets boiling on superhydrophilic textured substrates using high-speed X-ray phase contrast imaging (XRPCI). Our observations show that the transient bubble density variation follows similar characteristics irrespective of the texture spacing at a given surface temperature. For an example microstructure, we found that the number of discrete bubbles on the surface decreases as temperature is increased although their growth rate increases. Here, we observe that bubble growth is highly non-uniform during the lifetime of a drop on the surface. Initially, bubbles grow under diffusion-limited regime, but at later times they grow as ~t 1.45 due to combined effects of coalescence and evaporation. In some conditions, we found that bubbles shrink dramatically af- ter the initial growth spurt presumably due to severe quenching of the surface, and migration of bubbles on the surface. Using the bubble sizes, for the first time we analyzed the heat flux removed by a single bubble and also by all the bubbles at a given time. We find that the highest dissipation through latent- heat component (~600 W/cm 2 ) occurs just in the beginning and thereafter it decreases. We expect that our findings and the analysis would guide further work on the topic and will aid in the overarching goal of engineering surfaces that are more efficient in boiling heat transfer.

42 ENGINEERING↗

Training material models using gradient descent algorithms

High temperature design requires accurate constitutive models to describe material inelastic deformation and failure behavior. Oftentimes, calibrating accurate models devolves into the problem of fitting the model parameters against experimental test data. Here, we present the pyopmat package, an open source framework for calibrating constitutive models against experiment data subjected to various loading conditions using machine learning techniques. The package calculates the exact gradient of the model response with respect to the parameters using a combination of automatic differentiation and the adjoint method. Given this exact gradient, we compare the performance of several gradient-based optimization techniques in fitting realistic constitutive models against data. Here, we demonstrate the efficiency and accuracy of our package through example problems using both synthetic data, generated using known parameter sets, under monotonic and cyclic loading conditions and also with an example applying the techniques developed here to actual high temperature creep-fatigue test data.

36 MATERIALS SCIENCE↗

On plasticity-enhanced interfacial toughness in bonded joints

Here, the performance and reliability of many structures and components depend on the integrity of interfaces between dissimilar materials. Interfacial toughness Γ is the key material parameter that characterizes resistance to interfacial crack growth, and Γ is known to depend on many factors including temperature. For example, previous work showed that the toughness of an epoxy/aluminum interface decreased 40 % as the test temperature was increased from -60 °C to room temperature (RT). Interfacial integrity at elevated temperatures is of considerable practical importance. Recent measurements show that instead of continuing to decrease with increasing temperature, Γ increases when test temperature is above RT. Cohesive zone finite element calculations of an adhesively bonded, asymmetric double cantilever beam specimen of the type used to measure Γ suggest that this increase in toughness may be a result of R-curve behavior generated by plasticity-enhanced toughening during stable subcritical crack growth with interfacial toughness defined as the critical steady-state limit value. In these calculations, which used an elastic-perfectly plastic epoxy model with a temperature-dependent yield strength, the plasticity-enhanced increase in Γ above its intrinsic value Γ o depended on the ratio of interfacial strength σ* to the yield strength σyb of the bond material. There is a nonlinear relationship between Γ/Γ o and σ*/σ yb with the value Γ/Γ o increasing rapidly above a threshold value of σ*/σ yb . The predicted increase in toughness can be significant. For example, there is nearly a factor of two predicted increase in Γ/Γ o during micrometer-scale crack-growth when σ*/σ yb = 2 (a reasonable choice for σ*/σ yb ). Furthermore, contrary to other reported results, plasticity-enhanced toughening can occur prior to crack advance as the cohesive zone forms and the peak stress at the tip of the original crack tip translates to the tip of the fully formed cohesive zone. These results suggest that plasticity-enhanced toughening should be considered when modeling interfaces at elevated temperatures.

36 MATERIALS SCIENCE↗

Persistent compositions of non-stoichiometric compounds with low bulk diffusivity: A theory and application to Nb3Sn superconductors

Non-stoichiometric compounds may develop a composition gradient when they are formed by reactive diffusional processes. This paper reports an interesting phenomenon that in compounds with low bulk diffusivities, which rely mainly on grain boundary diffusion for their growth, the final bulk compositions may be far from equilibrium, with a very low bulk diffusivity leading to fixed (persistent) compositions – one such example is Nb 3 Sn, a superconductor. We investigated the microchemistry at the reactive interface using atom probe tomography to clarify the diffusion reaction mechanism for this low-bulk-diffusivity case and thus propose a theory for what determines the compound composition, using Nb 3 Sn as an example for concreteness. Using certain approximations, we derive an explicit analytical equation that illustrates what factors determine its composition profile. We compare our model with the known facts of Nb 3 Sn and see good agreement. In particular, this model predicts that internal oxidation may lead to higher Sn contents than conventional, non-oxidized Nb 3 Sn. Our measurements show that this is indeed true, and that the internally-oxidized Nb 3 Sn also has higher upper critical fields, achieving up to 28.2 T at 4.2 K. We discuss the general applicability of this model to non-stoichiometric compounds with low bulk diffusivity, and propose it as a tool to help in the design and processing of such materials for compositional control.

36 MATERIALS SCIENCE↗

A generalized approximate control variate framework for multifidelity uncertainty quantification

We describe and analyze a variance reduction approach for Monte Carlo (MC) sampling that accelerates the estimation of statistics of computationally expensive simulation models using an ensemble of models with lower cost. These lower cost models — which are typically lower fidelity with unknown statistics — are used to reduce the variance in statistical estimators relative to a MC estimator with equivalent cost. We derive the conditions under which our proposed approximate control variate framework recovers existing multifidelity variance reduction schemes as special cases. We demonstrate that existing recursive/nested strategies are suboptimal because they use the additional low-fidelity models only to efficiently estimate the unknown mean of the first low-fidelity model. As a result, they cannot achieve variance reduction beyond that of a control variate estimator that uses a single low-fidelity model with known mean. However, there often exists about an order-of-magnitude gap between the maximum achievable variance reduction using all low-fidelity models and that achieved by a single low-fidelity model with known mean. We show that our proposed approach can exploit this gap to achieve greater variance reduction by using non-recursive sampling schemes. The proposed strategy reduces the total cost of accurately estimating statistics, especially in cases where only low-fidelity simulation models are accessible for additional evaluations. Several analytic examples and an example with a hyperbolic PDE describing elastic wave propagation in heterogeneous media are used to illustrate the main features of the methodology.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling

Scale bridging is a critical need in computational sciences, where the modeling community has developed accurate physics models from first principles, of processes at lower length and time scales that influence the behavior at the higher scales of interest. However, it is not computationally feasible to incorporate all of the lower length scale physics directly into upscaled models. This is an area where machine learning has shown promise in building emulators of the lower length scale models, which incur a mere fraction of the computational cost of the original higher fidelity models. We demonstrate the use of machine learning using an example in materials science estimating continuum scale parameters by emulating, with uncertainties, complicated mesoscale physics. Additionally, we describe a new framework to emulate the fine scale physics, especially in the presence of microstructures, using machine learning, and showcase its usefulness by providing an example from modeling fracture propagation. Our approach can be thought of as a data-driven dimension reduction technique that yields probabilistic emulators. Our results show well-calibrated predictions for the quantities of interests in a low-strain simulation of fracture propagation at the mesoscale level. Furthermore, on average, we achieve ~10% relative errors on time-varying quantities like total damage and maximum stresses. Successfully replicating mesoscale scale physics within the continuum models is a crucial step towards predictive capability in multi-scale problems.

36 MATERIALS SCIENCE↗