Search NASA⌕ Search

SEARCH · Search NASA

Results for “broader”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Time-resolved electrical potential pump – X-ray photoelectron spectroscopy probe developments for investigating dynamic processes occurring at electrochemical interfaces

Electrode–electrolyte interfaces are of critical importance in several fields, including renewable energy, corrosion, and environmental chemistry. However, investigating these interfaces under operational conditions poses considerable challenges due to the limitations of the instrumentation employed. While recent advancements in in situ and operando techniques have enhanced our comprehension of the steady-state properties of solid-liquid interfaces, the dynamic behaviors of these systems remain inadequately explored. This study introduces a time-resolved X-ray photoelectron spectroscopy (XPS) technique designed to capture transient reaction intermediates and charging dynamics at electrified interfaces. The presented proof-of-principle study demonstrates that electrochemical processes, represented by an equivalent electrical circuit (EEC) model, can be probed and understood using square wave voltage pulses of a potentiostat synchronized to the modified data acquisition of an XPS setup. This method offers a valuable alternative to traditional pump–probe techniques, facilitating the investigation of a broader range of electrochemical systems. A dedicated software package for analyzing time- and energy-resolved XPS with a focus on extracting parameters of the EEC is geared towards benchmarking different EECs in future real-world electrochemical experiments.

Electrochemistry↗
DOE OSTI2022

Machine learning for surrogate process models of bioproduction pathways

Technoeconomic analysis and life-cycle assessment are critical to guiding and prioritizing bench-scale experiments and to evaluating economic and environmental performance of biofuel or biochemical production processes at scale. Traditionally, commercial process simulation tools have been used to develop detailed models for these purposes. However, developing and running such models can be costly and computationally intensive, which limits the degree to which they can be shared and reproduced in the broader research community. This study evaluates the potential of an automated machine learning approach to develop surrogate models based on conventional process simulation models. The analysis focuses on several high-value biofuels and bioproducts for which pathways of production from biomass feedstocks have been well-established. The results demonstrate that surrogate models can be an accurate and effective tool for approximating the cost, mass and energy balance outputs of more complex process simulations at a fraction of the computational expense.

09 BIOMASS FUELS↗
DOE OSTI2025

Allosteric prediction via convolutional neural networks and protein structural and dynamical features

Allostery is the phenomenon whereby a binding event or covalent modification at one site in a protein modulates function at a distal site, thus changing a protein’s functional state. As such, it is a ubiquitous aspect of protein functional regulation. Computationally predicting allosteric states is important as part of the broader challenge of functional annotation, but it also has practical implications for drug development, as targeting an allosteric site often affords greater specificity compared with targeting an orthosteric site. This study introduces a machine learning approach to predict the allosteric functional state using the small G-protein KRas as the model system, due to its implication in many types of cancer and being well studied as a result with many x-ray crystallographic structures of KRas available with different mutations and ligands bound. Using structural and dynamical features that can be cast as images, namely interatomic distances, contact maps, covariance, and mutual information, supervised learning was performed using convolutional neural networks. Two pretrained convolutional neural network architectures, GoogLeNet and ResNet18, were fine-tuned to classify KRas into active or inactive states based on these features. Across training regimes, atomic contact maps emerged as the most effective structural feature, whereas linearized mutual information outperformed covariance in capturing dynamical correlations relevant to allostery. Models achieved significant validation accuracy, with atomic contact maps yielding up to 90% accuracy. In conclusion, the findings suggest that integrating global structural rearrangements and correlated motion patterns with deep learning can reliably predict protein allosteric states, offering a promising framework for understanding allosteric regulation and developing targeted therapeutics.

Rajeshwar T., Rajitha [Oak Ridge National Laborato↗
DOE OSTI2023

Progressive evolutions of non-fused ring electron acceptors toward efficient organic solar cells with improved photocurrent and reduced energy loss

Resolving competitive counterbalance between high external quantum efficiency (EQE) response and low energy loss is critical for achieving efficient organic solar cells (OSCs), including those based on low-cost non-fused-ring electron acceptor (NFREA). In this work, we performed a comparable study by utilizing four NFREAs with progressive evolutions in molecular structures, energetic levels, spectral coverages, crystallinity, etc, to pair with polymer donor J52. By regulating the terminal and side chains of NFREAs, the enhanced intramolecular charge transfer (ICT) effect allows O-PC-EH possessing the broader spectral coverage for higher photocurrent, the reduced energetic offset enables O-PC-EH-based device owning higher luminescence property for lower energy loss, the strengthened crystallinity endows O-PC-EH-based blend better charge transport property for less charge recombination. With those comprehensive managements from molecule to blend properties, the J52:O-PC-EH-based OSCs achieved the best efficiency of 14.4% (certified: 14.1%) with benefits in all the three device parameters. Here, this work reveals the importance of fine-tuning molecular structures of NFREAs according to paired polymer donor for maximizing the device performances.

14 SOLAR ENERGY↗
DOE OSTI2023

Mining metatranscriptomes reveals a vast world of viroid-like circular RNAs

Viroids and viroid-like covalently closed circular (ccc) RNAs are minimal replicators that typically encode no proteins and hijack cellular enzymes for replication. The extent and diversity of viroid-like agents are poorly understood. We developed a computational pipeline to identify viroid-like cccRNAs and applied it to 5,131 metatranscriptomes and 1,344 plant transcriptomes. The search yielded 11,378 viroid-like cccRNAs spanning 4,409 species-level clusters, a 5-fold increase compared to the previously identified viroid-like elements. Within this diverse collection, we discovered numerous putative viroids, satellite RNAs, retrozymes, and ribozy-like viruses. Diverse ribozyme combinations and unusual ribozymes within the cccRNAs were identified. Self-cleaving ribozymes were identified in ambiviruses, some mito-like viruses and capsid-encoding satellite virus-like cccRNAs. The broad presence of viroid-like cccRNAs in diverse transcriptomes and ecosystems implies that their host range is far broader than currently known, and matches to CRISPR spacers suggest that some cccRNAs replicate in prokaryotes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Effect of a novel starch-based temperature rise inhibitor on cement hydration and microstructure development: The second peak study

Temperature rise inhibitor (TRI) modifies the exothermic process of cement hydration at early age and mitigates the buildup of cumulative heat, which sheds new light on solving the thermal cracking issue. After the main hydration peak, a broader “second peak” appears and brings the cumulative heat back to normal level within 7 days (for additions less than 0.15%). This study shows that the “second peak” can be attributed, almost exclusively, to C{sub 3}S hydration while triggered by C{sub 3}A hydration. In a pure C{sub 3}S system, the heat flow is depressed by TRI during the main hydration peak but never recovers after. In a monophase system composed of C{sub 3}S, C{sub 3}A and gypsum or a real cement paste, the ongoing precipitation of ettringite helps to pump out TRI polymer from the pore solution and eliminate the restriction on C-S-H nucleation. Afterwards, the precipitation of C-S-H accelerates again to form the “second peak”.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Evidence of formation of an amorphous magnesium silicate (AMS) phase during alkali activation of (Na-Mg) aluminosilicate glasses

Highlights: • No hydrotalcite-group phase is formed as Al preferentially incorporates into zeolites. • Mg forms an amorphous Mg silicate (AMS) phase. • AMS is a more depolymerized phase when compared to Mg silicate hydrate (M-S-H). • High charge density of Mg effectively stabilizes depolymerized silicates in AMS phase. • Existence of AMS phase should be considered as possibility in Mg-rich AAMs. There is some ambiguity regarding the fate of Mg during the alkali activation of Mg-rich precursors within the broader field of alkali activated materials (AAMs). The present work addresses this issue by studying the reaction products in AAMs synthesized from (Na-Mg) aluminosilicate glasses. Here, instead of magnesium silicate hydrate (M-S-H) phase, Mg exclusively forms an amorphous magnesium silicate (AMS) phase. Compared to M-S-H, AMS is a more depolymerized phase, which has not previously been well documented. The formation of AMS seems to be driven by the high charge density of the Mg cation which effectively stabilizes the depolymerized silicate species. We also show that the lack of hydrotalcite-group phases is due to aluminum depletion by zeolite formation. This work highlights the need to consider the existence of the AMS phase in Mg-containing AAMs, especially in complex systems, where its identification may be difficult.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Iron precipitation under controlled oxygen flow: Mineralogical implications for BIF precursors in the Archean ocean

Banded iron formation (BIF) deposition in the Archean ocean is thought to have been initiated by the oxidation of dissolved iron into ferric primary precursor phases that descended through the water column and were deposited on the seafloor. Effective interpretation of the trace element and isotopic composition of BIFs in the geologic record requires an understanding of the identity and longevity of those precursor phases. Temporal and spatial variation in oxidant concentration and ocean chemistry may have driven precursor mineralogy. Furthermore, precursor phases may undergo transitions during descent and burial. This experimental study follows the evolution of iron mineralogy, speciation, and redox state during the precipitation of iron under variable oxygen fluxes and in fluids containing different iron-complexing anions (chloride, sulfate, and phosphate). Additionally, suspensions collected after incomplete oxidation were anoxically aged to simulate changes to precursor mineralogy during descent below the seawater redoxcline. Results from X-ray diffraction, sequential dissolution, colorimetric determination of Fe redox state, and Fe K-edge X-ray absorption fine structure spectroscopy showed that under all experimental conditions, intermediate precipitates collected before complete iron uptake contained mineralized ferrous iron. Purely ferric mineral assemblages were not observed until iron was removed from solution. In low-oxygen experiments, intermediate precipitates contained phases with stoichiometric Fe 2+ , including magnetite, vivianite, and green rust (GR). Chloride green rust (GR1) had more ferric iron than sulfate green rust (GR2) and more readily transitioned to magnetite via disproportionation. GR2 was stable over a broader range of iron uptake; magnetite appeared mainly as an additional oxidation product in sulfate solution. In high oxygen experiments, mineralized ferrous iron occurred in amorphous phases or as a non-stoichiometric component in ferric oxides; final ferric assemblages were less crystalline than in low-oxygen equivalents. Low concentrations of phosphate increased the total iron oxidation rate, but also increased the ferrous content of intermediate precipitates. The incorporation of phosphate stabilized GR2 and facilitated GR2 precipitation over a larger range of iron uptake. In higher concentrations of phosphate, vivianite and poorly crystalline ferric hydroxides formed in place of GR. Here, these findings suggest that over a range of possible ocean chemistries, mixed-valence phases may have been short lived but relevant precursors to BIF. Additionally, anionic chemistry and oxidant concentration are shown to influence the crystallinity and chemical resistivity of final ferric assemblages, affecting their reactivity during later anoxic burial.

58 GEOSCIENCES↗
DOE OSTI2023

High-dimensional isotomics, part 2: Observations of over 100 constraints on methionine's isotome

The abundances of different isotopic forms of a compound, or isotopologues, will vary based on its physical and chemical history. The number of isotopologues increases combinatorically with the size of a molecule, and even small molecules such as amino acids have thousands of potentially observable isotopic variants. However, due to the analytical challenges of separating and observing isotopologues, only a few dimensions of isotopic diversity are routinely measured. Overcoming these challenges requires both an experimental method to observe many isotopic properties and a theoretical framework for interpreting these experiments. In Part 1, we presented such a theoretical framework; here, we demonstrate an experimental method, which we apply to methionine. Our approach uses a Q Exactive HF Orbitrap to perform several “M + N experiments”, where a sample is ionized, a subset of its isotopologues with cardinal mass N daltons greater than the unsubstituted isotopologue is selected and fragmented, and the proportions of all detectable isotopic forms of those fragment ions are quantified. We perform M + 1, M + 2, M + 3, and M + 4 experiments of a methionine sample and standard where the sample has a 100 ‰ enrichment of 13C at the methyl carbon relative to the natural 13C abundance at that position in the standard, and is otherwise identical to the standard. We observe isotopic forms of 8 fragment ion species for each version of the M + N experiment. With the assistance of a forward model of expected mass spectra, we identify isotopic peaks for each fragment ion based on observed mass and abundance, screen these for data quality, and quantify abundances for 146 unique isotopic peaks at precisions of ≈ 0.3–3 ‰. We present our direct observations and use them to reconstruct the concentrations of 19 individual singly, doubly, and triply-substituted isotopologues; doing so gives fewer constraints and broader error bars than working with the direct observations, but may be more interpretable for some applications. We also examine possibilities for measuring additional peaks, which are primarily limited by the detection limit of the Orbitrap-IRMS method. We then suggest some possible uses of our direct measurements for chemical forensics and hypothesis testing. Furthermore, our results demonstrate the diversity of isotopic constraints currently observable and interpretable for organic molecules.

58 GEOSCIENCES↗
DOE OSTI2026

Improving productivity and stability for CO 2 hydrogenation by using pincer-ligated Mn complexes with hemilabile ligands

Pincer ligands are widely used to support transition metal catalysts, but first-row systems often deactivate too quickly for widespread use. Here, pincer ligands bearing an additional hemilabile donor were designed to stabilize Mn catalysts for CO 2 hydrogenation to formate. Ligands of the type (RCH 2 CH 2 )N(CH 2 CH 2 P i Pr 2 ) 2 ( iPr PN R P; R = OMe, CH 2 OMe, NMe 2 , P i Pr 2 ) were synthesized and coordinated to Mn to form complexes of the type ( iPr PN R P)Mn(CO) 2 H. Their performance was compared to {MeN(CH 2 CH 2 P i Pr 2 ) 2 }Mn(CO) 2 H, which lacks a pendant donor. The pendant donor identity and arm length strongly influenced catalytic activity, but all pendant arm containing systems extended catalyst lifetime. Notably, ( iPr PN CH2OMe P)Mn(CO) 2 H achieved turnover frequencies of 158,000 h -1 and turnover numbers of 838,000, exceeding most state-of-the-art catalysts. Mechanistic studies revealed that hemilabile coordination enhances stability by avoiding deactivation, although excessive binding can lead to inactive states. Here, this work demonstrates hemilabile donors dramatically improve pincer-based Mn catalysts and provides design principles for broader applications.

CO2 hydrogenation↗
DOE OSTI2023

Virion glycosylation influences mycobacteriophage immune recognition

Glycosylation of eukaryotic virus particles is common and influences their uptake, trafficking, and immune recognition. In contrast, glycosylation of bacteriophage particles has not been reported; phage virions typically do not enter the cytoplasm upon infection, and they do not generally inhabit eukaryotic systems. We show here that several genomically distinct phages of Mycobacteria are modified with glycans attached to the C terminus of capsid and tail tube protein subunits. These O-linked glycans influence antibody production and recognition, shielding viral particles from antibody binding and reducing production of neutralizing antibodies. Glycosylation is mediated by phage-encoded glycosyltransferases, and genomic analysis suggests that they are relatively common among mycobacteriophages. Putative glycosyltransferases are also encoded by some Gordonia and Streptomyces phages, but there is little evidence of glycosylation among the broader phage population. The immune response to glycosylated phage virions in mice suggests that glycosylation may be an advantageous property for phage therapy of Mycobacterium infections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Rapid design of bacteriophage cocktails to suppress the burden and virulence of gut-resident carbapenem-resistant Klebsiella pneumoniae

Antibiotic use can lead to the expansion of multi-drug-resistant pathobionts within the gut microbiome that can cause life-threatening infections. Selective alternatives to conventional antibiotics are in dire need. Here, in this work, we describe a Klebsiella PhageBank for the tailored design of bacteriophage cocktails to treat multi-drug-resistant Klebsiella pneumoniae. Using a transposon library in carbapenem-resistant K. pneumoniae, we identify host factors required for phage infection in major Klebsiella phage families. Leveraging the diversity of the PhageBank, we formulate phage combinations that eliminate K. pneumoniae with minimal phage resistance. Optimized cocktails selectively suppress the burden of K. pneumoniae in the mouse gut and drive the loss of key virulence factors that act as phage receptors. Phage-mediated diversification of bacterial populations in the gut leads to co-evolution of phage variants with higher virulence and broader host range. Altogether, the Klebsiella PhageBank charts a roadmap for phage therapy against a critical multidrug-resistant human pathogen.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Implementation and evaluation of multi-dual mode counter-current chromatography in the CUP Modeler software

Counter-current chromatography (CCC) is a separation technique that utilizes immiscible solvent pairs as stationary and mobile phases, which imparts numerous benefits compared to solid-liquid chromatography including the ability to treat either the more-dense or less-dense solvent layer as the mobile phase. Multi-dual mode (MDM) is a CCC elution mode capable of improving the separation of closely eluting compounds by alternating upper- and lower-layer solvent flows in opposing directions within the same separation. While some effort has been made to model MDM, implementation of these models in experimental design has yet to be widely adopted. Accordingly, we further developed our previously published cell utilized partitioning (CUP) model to include MDM predictions with CCC and packaged the full suite of CUP modeling capabilities into a user-friendly, open-source tool called the CUP Modeler. The mathematical model for MDM CCC was derived and validated with experimental separation of ethyl guaiacol (EG) and ethyl phenol (EP), two compounds that co-elute in our previously demonstrated reductive catalytic fractionation (RCF) lignin monomer isolation method. The developed MDM model provided insights into the effect of multiple operating parameters - including stationary phase retention, flow rate, column efficiency, feed concentration ratio, selectivity factor, and solute distribution ratios - on the separation yields, productivity, and purities. Our model agreed with prevailing understanding of MDM but also revealed new insights including that the ideal distribution ratios for co-eluting solutes to be separated by MDM is between 1.1 and 1.5, with the lower value ideally close to 1.25. Overall, this work provides fundamental insights for MDM process design and enables broader adoption of general liquid-liquid chromatography with a new, open-source user-friendly interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics

Data-driven constitutive modeling with neural networks has received increased interest in recent years due to its ability to easily incorporate physical and mechanistic constraints and to overcome the challenging and time-consuming task of formulating phenomenological constitutive laws that can accurately capture the observed material response. However, even though neural network-based constitutive laws have been shown to generalize proficiently, the generated representations are not easily interpretable due to their high number of trainable parameters. Sparse regression approaches exist that allow for obtaining interpretable expressions, but the user is tasked with creating a library of model forms which by construction limits their expressiveness to the functional forms provided in the libraries. Here, in this work, we propose to train regularized physics-augmented neural network-based constitutive models utilizing a smoothed version of $L^0$-regularization. This aims to maintain the trustworthiness inherited by the physical constraints, but also enables interpretability which has not been possible thus far on any type of machine learning-based constitutive model where model forms were not assumed a priori but were actually discovered. During the training process, the network simultaneously fits the training data and penalizes the number of active parameters, while also ensuring constitutive constraints such as thermodynamic consistency. We show that the method can reliably obtain interpretable and trustworthy constitutive models for compressible and incompressible hyperelasticity, yield functions, and hardening models for elastoplasticity, using synthetic and experimental data. This work aims to set a new paradigm for interpretable machine learning models in the broad area of solid mechanics where low and limited data is available along with prior knowledge of physical constraints that the learned maps need to obey. This paradigm can potentially be extended to a broader spectrum of scientific exploration.

Data-driven constitutive models↗
DOE OSTI2024

Direct numerical simulation of a high-pressure hydrogen micromix combustor: Flame structure and stabilisation mechanism

A high-pressure hydrogen micromix combustor has been investigated using direct numerical simulation with detailed chemistry to examine the flame structure and stabilisation mechanism. The configuration of the combustor was based on the design by Schefer et al., using numerical periodicity to mimic a large square array. A precursor simulation of an opposed jet-in-crossflow was first conducted to generate appropriate partially-premixed inflow boundary conditions for the subsequent reacting simulation. The resulting flame can be described as an predominantly-lean inhomogeneously-premixed lifted jet flame. Five main zones were identified: a jet mixing region, a core flame, a peripheral flame, a recirculation zone, and combustion products. The core flame, situated over the jet mixing region, was found to burn as a thin reaction front, responsible for over 85% of the total fuel consumption. The peripheral flame shrouded the core flame, had low mean flow with high turbulence, and burned at very lean conditions (in the distributed burning regime). It was shown that turbulent premixed flame propagation was an order-of-magnitude too slow to stabilise the flame at these conditions. Stabilisation was identified to be due to ignition events resulting from turbulent mixing of fuel from the jet into mean recirculation of very lean hot products. Ignition events were found to correlate with shear-driven Kelvin-Helmholtz vortices, and increased in likelihood with streamwise distance. At the flame base, isolated events were observed, which developed into rapidly burning flame kernels that were blown downstream. Further downstream, near-simultaneous spatially-distributed ignition events were observed, which appeared more like ignition sheets. The paper concludes with a broader discussion that considers generalising from the conditions considered here.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Harnessing graph convolutional neural networks for identification of glassy states in metallic glasses

Graph Convolutional Neural Networks (GCNNs) have emerged as powerful tools for analyzing materials. In this study, we employ GCNNs to examine structural characteristics of CuZr metallic glasses (MGs) and identify their states. We use molecular dynamics to simulate the quenching process of CuZr, using cooling rates ranging from 10 9 to 10 15 K/s, to produce six unique glassy states. For each state, we create a dataset comprising 1,800 distinct samples. We evaluate the effectiveness of various GCNNs, including Graph Attention Neural Network (GANN), Graph Sample and AggreGatE (GraphSAGE), Graph Isomorphism Network (GIN), and Relational Graph Convolutional Neural Network (RGCN). GANN and GraphSAGE demonstrate comparable performance, achieving an overall accuracy of 81% in classifying the MG states. Furthermore, these results underscore the potential of GCNNs to detect subtle structural variances in disordered materials and point to broader application of deep learning in the analysis of MGs and other amorphous substances.

36 MATERIALS SCIENCE↗
DOE OSTI2025

ExaCA v2.0: A versatile, scalable, and performance portable cellular automata application for additive manufacturing solidification

The previously established ExaCA software for performance portable alloy grain structure simulation has been updated to better represent the solidification behavior during complex alloy processing conditions, such as those encountered during metal additive manufacturing (AM), and for improved performance and scalability. Here, an extension to the time–temperature history input data format and the core ExaCA algorithm to include an arbitrary number of melting and solidification events yielded improved prediction of texture for various melt pool geometries, expanding the range of AM-relevant conditions that can be accurately simulated. Improved heat transport process simulation coupling, including the creation of large raster datasets from single track time–temperature history data and in-memory coupling with the new, performance portable finite difference code Finch, were also demonstrated in example studies on the effect of multilayer AM microstructure predictions on hatch spacing and cell size, respectively. Additional new features are detailed and demonstrated, including the ability to perform simulations using various interfacial response function forms, execute simulations on state-of-the-art hardware, improved usability through post-processing versatility, and improved strong and weak scaling performance. The performance, physics, and versatility improvements demonstrated here will further enable large-scale studies on AM process–microstructure relationships that were not previously possible. Furthermore, the usability improvements and ability to run coupled AM process–microstructure simulations using the Finch-ExaCA workflow will facilitate broader use of this open-source software by the computational materials community.

36 MATERIALS SCIENCE↗
DOE OSTI2025

From clutter to clarity: Emergent neural operators via questionnaire metrics

Real-world datasets in chemical engineering and bioengineering processes—such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials—can often be unlabeled or disorganized, rendering the training of existing supervised learning models ineffective at learning the underlying dynamics. To salvage these datasets for decision-making, we first seek to obtain clarity from the cluttered data. Here, we present a framework for developing “structural” generative models, discovering emergent equations, and constructing efficient emulators from scrambled datasets by integrating unsupervised organizational learning techniques (Questionnaires) with advanced deep learning architectures (Deep Hidden Physics Models and Deep Operator Networks). Our approach is demonstrated on two illustrative model systems: (a) a 1D advection–diffusion partial differential equation representing a winding underground pipe and (b) an ensemble of Stuart–Landau oscillators, an agent-based system of coupled ordinary differential equations. In both cases, we successfully reconstruct meaningful spatial, temporal, and parameter embeddings from scrambled data, enabling good predictions of system dynamics. As a result, we highlight the framework’s potential for broader applications, enabling data-driven system identification in fields with inherently disorganized or hidden parameter spaces.

42 ENGINEERING↗