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DOE OSTI2023

Revealing the Milky Way’s most recent major merger with a Gaia EDR3 catalogue of machine-learned line-of-sight velocities

Machine learning can play a powerful role in inferring missing line-of-sight velocities from astrometry in surveys such as Gaia. In this paper, we apply a neural network to Gaia Early Data Release 3 (EDR3) and obtain line-of-sight velocities and associated uncertainties for ~92 million stars. The network, which takes as input a star’s parallax, angular coordinates, and proper motions, is trained and validated on ~6.4 million stars in Gaia with complete phase-space information. The network’s uncertainty on its velocity prediction is a key aspect of its design; by properly convolving these uncertainties with the inferred velocities, we obtain accurate stellar kinematic distributions. As a first science application, we use the new network-completed catalogue to identify candidate stars that belong to the Milky Way’s most recent major merger, Gaia-Sausage-Enceladus (GSE). We present the kinematic, energy, angular momentum, and spatial distributions of the ~450 000 GSE candidates in this sample, and also study the chemical abundances of those with cross matches to GALAH and APOGEE. Furthermore, the network’s predictive power will only continue to improve with future Gaia data releases as the training set of stars with complete phase-space information grows. This work provides a first demonstration of how to use machine learning to exploit high-dimensional correlations on data to infer line-of-sight velocities, and offers a template for how to train, validate, and apply such a neural network when complete observational data is not available.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Intra-hour Solar Irradiance Forecast in Multiple Locations using Deep Transfer Learning

In recent years, solar power system installation imposes several challenges on the operations of local and regional power grids due to the inherent variability of ground-level solar irradiance. This work proposes a novel real-time solar forecast methodology for intra-hour solar irradiance based on deep transfer learning from ground-based sky imager for time horizons ranging from 5-15 min. There are three unique aspects of the proposed methodology: (1) a Deep Learning based algorithm development which is modeled as a classification approach rather than a traditional regression approach; (2) the use of the Transfer Learning technique to show generalization capability, robustness, and portability of baseline model in the newly deployed location where availability of enough data for training is typically scarce, and (3) redefinition of point-based irradiation forecast error estimation technique with a window-based one that is more intuitive and user-friendly. The system is developed using multiple years of irradiance and sky image recording in New Jersey and one-year data from Colorado, USA. The method is validated against ground telemetry from these two locations of diverse geographic and climatic conditions. Results show that the forecasting method proposed in this work is robust and highly accurate (8% MAPE error) for multiple locations deployment.

Deep Learning, Convolution Neural Networks, transf↗

DOE OSTI2020

14 MeV DT Neutron Test Facility at the Sandia Ion Beam Laboratory

A recently completed LDRD project has provided a new facility at Sandia for testing effects of energetic neutrons on electronic components. 14 MeV neutrons are produced with a deuterium ion beam onto a thin-film tritide target. The goal of the project was to increase the neutron fluence to levels needed for radiation effects testing and qualification. This goal was achieved through two technical advances. First, a new multi-layer target concept was developed to reduce the rate of tritium loss from the target by isotope exchange, thereby reducing tritium usage and increasing target lifetime. The second advance was the construction of a new test chamber designed to maximize neutron flux at test locations. Together, these increased the available neutron fluence by several orders of magnitude. This new capability is being used in tests for Sandia nuclear weapon programs, evaluation of commercial parts such as highly-scaled CMOS SRAM lCs, and tests of new devices under development at Sandia such as lll-V HBTs, gallium nitride high-voltage diodes, and for fundamental studies of physical mechanisms of device failure.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Material and Design Optimization of Printed Melt Wire Arrays

Recent work conducted by the Advanced Sensors and Instrumentation (ASI) program at Idaho National Laboratory resulted in the establishment of in-house capabilities for fabricating and testing new advanced manufactured sensors for measuring irradiation temperatures inside a nuclear test reactor. Though current methods of real-time temperature monitoring (e.g., thermocouples) can still be used, the complexity of the feedthroughs and attachments needed for collecting real time measurements greatly increases the experiment-related costs. On the other hand, passive monitoring techniques can be used for collecting post irradiation temperature measurements by inferring reactor temperatures, based on the melting points of well-characterized materials (i.e., standard melt wires). However, challenges have arisen due to the limited space available for including instrumentation in experiments. To resolve this issue, the ASI program expanded its temperature detection capabilities to include advance manufactured melt wires for post-irradiation temperature measurements. These melt wires can determine reactor temperatures while also accommodating space limitations in irradiation experiments. To improve performance reliability and enhance melt wire readability following irradiation, FY-22 efforts have focused on optimizing the materials used in the encapsulation and printed melt wire array. This report details the design and fabrication tasks, along with the subsequent x ray computed tomography (XCT) evaluation process. The melt wire array consisted of indium with a melting point of 157°C, indium/silver (96/4 at%) with a melting point of 219°C, and tin with a melting point of 230°C. The encapsulation disc was made of vanadium due to its low activation properties and radiation resistance when deployed in nuclear reactors. Additionally, the melt wire design consisted of a ceramic sublayer (alumina disc) to further enhance the XCT post melting images of the printed melt wires. However, when sealing the vanadium container, all three melt wires melted, reflecting the temperature limitations that must be considered when employing metal containers in the sealing process.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Investigation of powertrain system decarbonization using electrically assisted turbocharging and hybridization in off-road vehicles

In recent years, the causes, effects, and existential threat of a global anthropogenic climate shift have drawn significant attention and stimulated mitigation efforts from all genres of scientific, political, and industrial bodies. The greenhouse effect and the detrimental environmental impact of excess greenhouse gas (GHG) emissions, like carbon dioxide, is well studied and targeted as the primary culprit for aversive action. However, some fields exist where reducing GHG emissions is met by considerable challenges. One such field is the production and operations of off-road vehicles. Applications of these vehicles are highly diverse and are often characterized by rugged, especially transient, and power intensive duty cycles that make one-fits-all vehicle configuration solutions impractical. This research surveys existing literature to identify the modern technologies packages and challenges that face development and configuration of powertrain systems which currently navigate the regulated emissions and unique duty cycle requirements of this market space. A novel powertrain concept is proposed and evaluated with respect to the pinnacle objectives of load performance improvement, criteria pollutant reduction, and reducing the life cycle GHG intensity of its operation. A sure-fire path to vehicular GHG reduction is through improving the fuel efficiency of internal combustion engines (ICEs), an entrenched component of the off-road vehicle sector. Unfortunately, this is more easily said than done. An approach that has proven successful in this endeavor is engine downsizing and turbocharging, where a larger engine is replaced with a smaller one with added air system boosting via turbocharger to reduce frictional and pumping losses while also enabling access to additional fuel energy. However, practical realization of these potential benefits is often impeded by the transient response capability of the smaller engine across the operating space. For this reason, downsized ICE powertrains have turned to electrified forced induction systems (EFISs) for a decoupling of exhaust energy and engine speed from boost capability. Platformed on 48V hybrid technology, these systems introduce the need for more sophisticated controls around the engine gas-exchange process for the management of boost performance and exhaust gas emissions. On this account, simulation studies utilizing a GT-POWER model of a turbocharged 4.5 L engine outfit with an EFIS using an electrically driven compressor (eBooster®) are conducted to provide insight into the performance of this air handling architecture on an off-road engine. The results show that improvement in transient torque response time in sync with reduction in engine-out soot and NOx emissions are possible with an engine recalibration that leverages the transient air-fuel ratio authority of the eBooster®. Benefits are further demonstrated when duty cycle simulations of the 48V mild-hybrid engine concept are exercised, showing an acute decrease in cumulative fuel usage and soot production. Powertrain hybridization is another technological pathway achieving pronounced GHG reduction successes in modern on-road vehicle applications through integration of Li-ion battery technology. In the off-road vehicle segment, a review of available literature concludes that hybridized architectures are present but generally lack the depth of technologies that have both high specific energy and power capabilities, and thus are limited in their inclusion of Li-ion batteries for ICE assistance and enhanced energy storage capability. Therefore, building on the mild-hybrid engine results, the downsized and eBoosted engine concept was integrated into a larger high-voltage battery-hybrid series-electric powertrain system. Hybrid powertrain parameter sensitivity studies were carried out in a numerical charge-sustaining framework, providing novel insights into power flows between the battery and the engine and how their respective capabilities and operation contribute to GHG and criteria pollutant emissions of diverse duty cycles. Application of supervisory power management introduced robustness into the power sourcing and battery SOC control process and showed that optimum specification of battery properties can yield synergies between GHG emissions and battery energy capacity. Furthermore, examination of recent literature on Li-ion batteries has shown that pack manufacturing is a highly energy intensive process, thereby producing considerable quantities of GHGs that scale with energy storage capacity. Also scaling with a battery’s energy storage capacity is its investment cost. In consideration of these factors, an inclusive technoeconomic and GHG life cycle analysis is conducted. This analysis systematically compares the carbon footprint and total cost of ownership associated with the proposed hybrid powertrain concept to reference and alternative powertrain configurations, facilitating a thorough evaluation of the decarbonization effectiveness and economic viability.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Relativistic diffusion model for hadron production in p -Pb collisions at energies available at the CERN Large Hadron Collider

We investigate charged-hadron production in relativistic heavy-ion collisions of asymmetric systems within a nonequilibrium-statistical framework. Calculated centrality-dependent pseudorapidity distributions for p-Pb collisions at s NN = 5.02 and 8.16 TeV are compared with data from the Large Hadron Collider. Our approach combines a relativistic diffusion model with formulations based on quantum chromodynamics while utilizing numerical solutions of a Fokker-Planck equation to account for the shift and broadening of the fragmentation sources for particle-production with respect to the stopping (net-baryon) rapidity distributions. To represent the centrality dependence of charged-hadron production in asymmetric systems over a broad region of pseudorapidities, the consideration and precise modeling of the fragmentation sources, along with the central gluon-gluon source, is found to be essential. Specifically, this results in an inversion of the particle-production amplitude from backward to forward dominance when transitioning from central to peripheral collisions, in agreement with recent ATLAS and ALICE p-Pb data at s NN = 5.02 T e V . Published by the American Physical Society 2024

Schulz, Philipp (ORCID:0009000266642148)↗

DOE OSTI2024

Why Does Atmospheric Radiative Heating Weaken Midlatitude Cyclones?

Abstract Recent work has indicated that atmospheric radiative heating reduces the kinetic energy of large‐scale eddies in the midlatitudes. However, a physical mechanism that connects radiation to the midlatitude eddy kinetic energy is still uncertain. Using a high‐resolution general circulation model we perform an experiment in which the radiative cooling profile at each model time step is overwritten with the climatological mean, computed from a control simulation. This approach separates the mean and transient effects of radiative heating on the extratropical circulation. We find that, when radiative heating is fixed, the globally‐averaged eddy kinetic energy is enhanced by ∼6%. We show that thermal radiation dampens temperature anomalies near the surface and tropopause in low‐pressure systems, destroying eddy available potential energy and eddy kinetic energy. We identify this as a possible mechanism by which atmospheric radiative heating weakens midlatitude cyclones.

Mischell, Eric↗

DOE OSTI2021

Low-temperature (< 200 °C) degradation of electronic nicotine delivery system liquids generates toxic aldehydes

Electronic cigarette usage has spiked in popularity over recent years. The enhanced prevalence has consequently resulted in new health concerns associated with the use of these devices. Degradation of the liquids used in vaping have been identified as a concern due to the presence of toxic compounds such as aldehydes in the aerosols. Typically, such thermochemical conversions are reported to occur between 300 and 400 °C. Herein, the low-temperature thermal degradation of propylene glycol and glycerol constituents of e-cigarette vapors are explored for the first time by natural abundance 13 C NMR and 1 H NMR, enabling in situ detection of intact molecules from decomposition. The results demonstrate that the degradation of electronic nicotine delivery system (ENDS) liquids is strongly reliant upon the oxygen availability, both in the presence and absence of a material surface. When oxygen is available, propylene glycol and glycerol readily decompose at temperatures between 133 and 175 °C over an extended time period. Among the generated chemical species, formic and acrylic acids are observed which can negatively affect the kidneys and lungs of those who inhale the toxin during ENDS vapor inhalation. Further, the formation of hemi- and formal acetals is noted from both glycerol and propylene glycol, signifying the generation of both formaldehyde and acetaldehyde, highly toxic compounds, which, as a biocide, can lead to numerous health ailments. The results also reveal a retardation in decomposition rate when material surfaces are prevalent with no directly observed unique surface spectator or intermediate species as well as potentially slower conversions in mixtures of the two components. The generation of toxic species in ENDS liquids at low temperatures highlights the dangers of low-temperature ENDS use.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Circuit-level design principles for transmission-mode microwave impedance microscopy

A recently developed technique of transmission-mode microwave impedance microscopy (T-MIM) has greatly extended the capabilities of standard reflection-mode MIM to novel applications, such as the in operando study of nanoscale electro-acoustic devices. As is common for new techniques, systematic design principles for boosting sensitivity and balancing bandwidth are lacking. Here, we show numerically and analytically that the T-MIM signal is proportional to the reflection-mode voltage enhancement factor η of the circuit, as long as the output impedance of the local voltage source is properly treated. We show that this proportionality holds in the currently achievable “weak sampling” regime and beyond, for which we demonstrate a realistic path with commercially available superconducting components and critically coupled impedance matching networks. Furthermore, we demonstrate that for these next-generation designs, the sensitivity is generally maximized at a slightly different frequency from the unloaded S 11 resonance, which can be explained by the maximum power transfer theorem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

H-lignin can be deposited independently of CINNAMYL ALCOHOL DEHYDROGENASE C and D in Arabidopsis

Abstract Lignin contributes substantially to the recalcitrance of biomass toward saccharification. To circumvent this problem, researchers have genetically altered lignin, although, in a number of cases, these efforts have resulted in an undesirable yield penalty. Recent findings have shown that by knocking out two subunits (MED5A and MED5B) of the transcriptional regulatory complex Mediator, the stunted growth phenotype of mutants in p-coumaroyl shikimate 3′-hydroxylase, reduced epidermal fluorescence 8-1 (ref8-1), can be alleviated. Furthermore, these plants synthesize a lignin polymer almost entirely derived from p-coumaryl alcohol. Plants deficient in cinnamyl alcohol dehydrogenase (CAD) are notable in that they primarily incorporate coniferaldehyde and sinapaldehyde into their lignin. We tested the hypothesis that by stacking mutations in the genes encoding for the CAD paralogs C and D on an Arabidopsis (Arabidopsis thaliana) med5a/5b ref8-1 genetic background, the biosynthesis of p-coumaryl alcohol would be blocked, making p-coumaraldehyde available for polymerization into a novel kind of lignin. The med5a/5b ref8-1 cadc cadd plants are viable, but lignin analysis demonstrated that they continue to synthesize p-hydroxyphenyl lignin despite being mutated for the CADs typically considered to be required for monolignol biosynthesis. In addition, enzyme activity tests showed that even in the absence of CADC and CADD, there is high CAD activity in stems. We tested the potential involvement of other CADs in p-coumaraldehyde biosynthesis in the quintuple mutant by mutating them using the CRISPR/Cas9 system. Lignin analysis demonstrated that the resulting hextuple mutant plants continue to deposit p-coumaryl alcohol-derived lignin, demonstrating a route for the synthesis of p-hydroxyphenyl lignin in Arabidopsis independent of four CAD isoforms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Genome Extraction from Shotgun Metagenome Sequence Data

Uncultivated Bacteria and Archaea comprise the vast majority of species on Earth, but obtaining their genomes directly from the environment, using shotgun sequencing, has only recently become possible. To realize the hope of capturing Earth’s microbial genetic complement, technologies that accelerate recovery of high-quality genomes are necessary. We present a series of analysis steps and data products for the extraction of high quality metagenome-assembled genomes (MAGs) from microbiomes using the U.S. Department of Energy Systems Biology Knowledgebase (KBase) platform (http://www.kbase.us/). In KBase, the process is end-to-end, allowing a user to go from the initial sequencing reads all the way through to MAG genomes, which can then be analyzed with other KBase capabilities such as phylogenetic placement, functional assignment, metabolic modeling, pangenome functional profiling, RNA-Seq, and others. While portions of such capabilities are individually available from other resources, the combination of the intuitive usability, data interoperability, and integration of tools in a freely available compute resource makes KBase a uniquely powerful platform for obtaining MAGs from microbiomes. While this workflow offers tools for each of the key steps in the genome extraction process, it also provides a scaffold that can be easily extended, with additional MAG recovery and analysis tools, via the KBase SDK (Software Development Kit).

Chivian, Dylan↗

DOE OSTI2021

Moab Desert Crust - Sample 4E

Chivian, Dylan↗

DOE OSTI2021

Occupancy-Driven Stochastic Decision Framework for Ranking Commercial Building Loads

For effective integration of building operations into the evolving demand response programs of the power grid, real-time decisions concerning the use of building appliances for grid services must excel on multiple criteria, ranging from the added value to occupants' comfort to the quality of the grid services. In this paper, we present a data-driven stochastic decision-support framework to dynamically rank load control alternatives in a commercial building, addressing the needs of multiple decision criteria (e.g. occupant comfort, grid service quality) under uncertainties in occupancy patterns. We adopt a stochastic multi-criteria decision algorithm recently applied to prioritize residential on/off loads, and extend it to i) consider complex load control decisions (e.g. dimming of lights, changing zone temperature set-points) in a commercial building; and ii) systematically integrate zonal occupancy patterns to better identify short-term (and time-varying) opportunities for grid service participation. We evaluate the performance of the proposed framework for curtailment of air-conditioning, lighting, and plug-loads in a multi-zone commercial office building for a range of design choices. With the help of a prototype system that integrates an interactive \textit{Data Analytics and Visualization} frontend we demonstrate a way for the building operators to monitor and change in real-time the available flexibility in energy consumption and to develop trust in the decision recommendations by interpreting the rationale behind the ranking.

Jain, Milan↗

DOE OSTI2024

Determination of Pion and Kaon Structure Using the Sullivan Process at Moderate to large Fractional Momentum

Significant progress has been made in describing the structure of hadrons in terms of Quantum ChromoDynamics (QCD) degrees of freedom. The recently developed framework of Generalized Parton Distribtions (GPDs) may provide the path towards three-dimensional imaging of the hadronic structure. Integrating the GPDs over the fractional momentum (x), the form factors are obtained which are integrally related to understanding the emergent hadron mass-generating mechanism (EHM) embedded in QCD and the mass-scale that characterizes the proton [Roberts (2016)]. The Sullivan process, in which one scatters electrons off the proton?s virtual meson cloud and thus creates a meson target, is used to probe this structure. Studies of kaon electroproduction offer unique opportunities in this. The last two decades saw a dramatic improvement in the precision of charged meson form factor data, and new results have become available on the transition form factor. The KaonLT experiment (E12-09-011) carried out in Hall C at Jefferson Lab (Jlab) was designed to measure longitudinal/transverse (L/T) separated kaon cross sections required to access form factors and GPDs [Bosted et al. (2008)]. KaonLT aimed to confirm the exclusive kaon electroproduction mechanism, which is essential for determining the kaon form factor and investigating GPDs. The results obtained from this experiment have provided the highest Q2 for L-T separated kaon electroproduction cross section, and it is the first time that a separated kaon cross-section measurement has been carried out above W = 2:2GeV.

Trotta, Richard↗

DOE OSTI2023

Advanced Materials and Additive Manufacturing for Phase Change Thermal Energy Storage and Management: A Review

Abstract Phase change materials (PCMs) can enhance the performance of energy systems by time shifting or reducing peak thermal loads. The effectiveness of a PCM is defined by its energy and power density—the total available storage capacity (kWh m −3 ) and how fast it can be accessed (kW m −3 ). These are influenced by both material properties as well as geometry of the energy systems; however, prior efforts have primarily focused on improving material properties, namely, maximizing latent heat of fusion and increasing thermal conductivity. The latter is often at the expense of the former. Advanced manufacturing techniques hold tremendous potential to enable co‐optimization of material properties and device geometry, while potentially reducing material waste and manufacturing time. There is an emerging body of research focused on additive manufacturing of PCM composites and devices for thermal energy storage (TES) and thermal management. In this article, the fundamentals and applications of PCMs are reviewed and recent additive manufacturing advances in latent heat TES for both the PCM composite and associated heat exchanger are discussed. A forward‐looking perspective on the future and potential of PCM additive manufacturing for TES and thermal management is provided.

25 ENERGY STORAGE↗

DOE OSTI2024

Predicting thermophysical properties of molten salts in the MgCl 2 -NaCl-KCl-LiCl system with a shell-model potential

Ternary eutectic salts composed of MgCl 2 , NaCl, and KCl, referred to as MNK salts, have recently emerged as promising candidates as high-temperature heat transfer fluids and thermal energy storage media. Here we performed classical molecular dynamics (MD) simulations to predict the densities, specific heat capacities, viscosities, and ionic self-diffusivities for MNK salts over a wide temperature range. The impact of LiCl additive on their thermophysical properties was also investigated. To capture the electronic polarization of Cl anions by neighboring cations, we developed a novel shell-model potential using the force-matching method and a dataset of ab initio calculated interatomic forces. Our extensive MD simulations predict structure and properties for pure salts and binary/ternary salt mixtures in the MgCl 2 -NaCl-KCl-LiCl system in overall good agreement with available experimental and theoretical data, which corroborates the accuracy and reliability of our developed potential.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Ordinary and exotic mesons in the extended Linear Sigma Model

The extended Linear Sigma Model (eLSM) is a hadronic model based on the global symmetries of QCD and the corresponding explicit, anomalous, and spontaneous breaking patterns. In its basic three-flavor form, its mesonic part contains the dilaton/glueball as well as the nonets of pseudoscalar, scalar, vector, and axial–vector mesons, thus chiral symmetry is linearly realized. In the chiral limit and neglecting the chiral anomaly, only one term – within the dilaton potential – breaks dilatation invariance, and all terms are chirally symmetric. Spontaneous symmetry breaking is implemented by a generalization of the Mexican-hat potential, with explicit symmetry breaking responsible for its tilting. The overall mesonic phenomenology up to ~2 GeV is in agreement with the PDG compilation of masses and partial and total decay widths. The eLSM was enlarged in a straightforward way to include other conventional quark–antiquark nonets (pseudovector and orbitally excited vector mesons, tensor and axial-tensor mesons, radially excited (pseudo)scalar mesons, etc.), as well as two nonets of hybrid mesons, the lightest one with exotic quantum numbers J $\mathscr{PC}$ = 1 -+ not allowed for $\bar{q}q$ objects, such as the resonance π 1 (1600) and the recently discovered η 1 (1855) . In doing so, different types of chiral multiplets are introduced: heterochiral and homochiral multiplets, which differ in the way they transform under chiral transformations. Moreover, besides the scalar glueball that is present from the beginning as dilaton, other glueballs, the tensor, the pseudoscalar and the vector glueballs were coupled to the eLSM: the scalar resonance ƒ 0 (1710) turns out to be mostly gluonic, the tensor glueball couples strongly to vector mesons, and the pseudoscalar glueball couples sizably to $ππη'$ and can be assigned to X (2370) or X (2600). In all cases above, masses and decays can be analyzed allowing for a better understanding of both conventional and non-conventional mesons: whenever data are available, a comparison is performed and, when this is not the case, predictions of decay widths and decay ratios are outlined. The eLSM contains chiral partners on an equal footing and is therefore well suited for studies of chiral symmetry restoration at nonzero temperature and densities: this is done by coupling it to the Polyakov loop. Finally, the QCD phase diagram and the location of the critical endpoint were investigated within this framework.

Chiral symmetry↗

DOE OSTI2024

A Mass‐Conserving‐Perceptron for Machine‐Learning‐Based Modeling of Geoscientific Systems

Although decades of effort have been devoted to building Physical-Conceptual (PC) models for predicting the time-series evolution of geoscientific systems, recent work shows that Machine Learning (ML) based Gated Recurrent Neural Network technology can be used to develop models that are much more accurate. However, the difficulty of extracting physical understanding from ML-based models complicates their utility for enhancing scientific knowledge regarding system structure and function. Here, we propose a physically interpretable Mass-Conserving-Perceptron (MCP) as a way to bridge the gap between PC-based and ML-based modeling approaches. The MCP exploits the inherent isomorphism between the directed graph structures underlying both PC models and GRNNs to explicitly represent the mass-conserving nature of physical processes while enabling the functional nature of such processes to be directly learned (in an interpretable manner) from available data using off-the-shelf ML technology. As a proof of concept, we investigate the functional expressivity (capacity) of the MCP, explore its ability to parsimoniously represent the rainfall-runoff (RR) dynamics of the Leaf River Basin, and demonstrate its utility for scientific hypothesis testing. To conclude, we discuss extensions of the concept to enable ML-based physical-conceptual representation of the coupled nature of mass-energy-information flows through geoscientific systems.

58 GEOSCIENCES↗