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The development of the protect-annealing method has extended the superconductivity of the electron-doped cuprate \PLCCO ~(PLCCO) into lower Ce concentrations, while the superconducting volume fraction decreases with underdoping. Employing angle-resolved photoemission spectroscopy with a micro-focused beam, we investigated the electronic structure of protect-annealed PLCCO ($x=0.08$) with small superconducting volume fraction. Significant spatial variation of Fermi surface area and shape was observed, suggesting inhomogeneity in electron concentrations and the pseudogap that competes with superconductivity. By performing measurements at dozens of different sample positions, negative and non-monotonic correlation was found between the electron concentration and pseudogap magnitude. The established correlation illustrates a systematic annealing dependence of the electronic structure where a pseudogap abruptly opens with insufficient oxygen reduction.

The symbiosis of strong interactions, flat bands, topology and symmetry has led to the discovery of exotic phases of matter, including fractional Chern insulators, correlated moir\'e topological superconductors, and Dirac and Weyl semimetals. Correlated metals, such as those present in Kondo lattices, rely on the screening of local moments by a sea of non-magnetic conduction electrons. Here, we report on a unique topological Kondo lattice compound, CeCo2P2, where the Kondo effect - whose existence under the magnetic Co phase is protected by PT symmetry - coexists with antiferromagnetic order emerging from the flat bands associated with the Co atoms. Remarkably, this is the only known Kondo lattice compound where magnetic order occurs in non-heavy electrons, and puzzlingly, at a temperature significantly higher than that of the Kondo effect. Furthermore, at low temperatures, the emergence of the Kondo effect, in conjunction with a glide-mirror-z symmetry, results in a nodal line protected by bulk topology near the Fermi energy. These unusual properties, arising from the interplay between itinerant and correlated electrons from different constituent elements, lead to novel quantum phases beyond the celebrated topological Kondo insulators and Weyl Kondo semimetals. CeCo2P2 thus provides an ideal platform for investigating narrow bands, topology, magnetism, and the Kondo effect in strongly correlated electron systems., Comment: 17pages,4 figures

The integration of machine learning techniques into Inertial Confinement Fusion (ICF) simulations has emerged as a powerful approach for enhancing computational efficiency. By replacing the costly Non-Local Thermodynamic Equilibrium (NLTE) model with machine learning models, significant reductions in calculation time have been achieved. However, determining how to optimize machine learning-based NLTE models in order to match ICF simulation dynamics remains challenging, underscoring the need for physically relevant error metrics and strategies to enhance model accuracy with respect to these metrics. Thus, we propose novel physics-informed transformations designed to emphasize energy transport, use these transformations to establish new error metrics, and demonstrate that they yield smaller errors within reduced principal component spaces compared to conventional transformations., Comment: 10 pages, 7 figures

We propose machine learning (ML) models to predict the electron density -- the fundamental unknown of a material's ground state -- across the composition space of concentrated alloys. From this, other physical properties can be inferred, enabling accelerated exploration. A significant challenge is that the number of sampled compositions and descriptors required to accurately predict fields like the electron density increases rapidly with species. To address this, we employ Bayesian Active Learning (AL), which minimizes training data requirements by leveraging uncertainty quantification capabilities of Bayesian Neural Networks. Compared to strategic tessellation of the composition space, Bayesian-AL reduces the number of training data points by a factor of 2.5 for ternary (SiGeSn) and 1.7 for quaternary (CrFeCoNi) systems. We also introduce easy-to-optimize, body-attached-frame descriptors, which respect physical symmetries and maintain approximately the same descriptor-vector size as alloy elements increase. Our ML models demonstrate high accuracy and generalizability in predicting both electron density and energy across composition space.

Layered transition metal dichalcogenides (TMDs) stabilize in multiple structural forms with profoundly distinct and exotic electronic phases. Interfacing different layer types is a promising route to manipulate TMDs' properties, not only as a means to engineer quantum devices, but also as a route to explore fundamental physics in complex matter. Here we use angle-resolved photoemission (ARPES) to investigate a strong layering-dependent enhancement of superconductivity in TaS$_2$, in which the superconducting transition temperature, $T_c$, of its $2H$ structural phase is nearly tripled when insulating $1T$ layers are inserted into the system. The study is facilitated by a novel vanadium-intercalation approach to synthesizing various TaS$_2$ polytypes, which improves the quality of ARPES data while leaving key aspects of the electronic structure and properties intact. The spectra show the clear opening of the charge density wave gap in the pure $2H$ phase and its suppression when $1T$ layers are introduced to the system. Moreover, in the mixed-layer $4Hb$ system, we observe a strongly momentum-anisotropic increase in electron-phonon coupling near the Fermi level relative to the $2H$ phase. Both phenomena help to account for the increased $T_c$ in mixed $2H$/$1T$ layer structures.

Large language models (LLMs) are increasingly employed in information-seeking and decision-making tasks. Despite their broad utility, LLMs tend to generate information that conflicts with real-world facts, and their persuasive style can make these inaccuracies appear confident and convincing. As a result, end-users struggle to consistently align the confidence expressed by LLMs with the accuracy of their predictions, often leading to either blind trust in all outputs or a complete disregard for their reliability. In this work, we explore supervised finetuning on uncertainty-augmented predictions as a method to develop models that produce linguistic expressions of uncertainty. Specifically, we measure the calibration of pre-trained models and then fine-tune language models to generate calibrated linguistic expressions of uncertainty. Through experiments on various question-answering datasets, we demonstrate that LLMs are well-calibrated in assessing their predictions, and supervised finetuning based on the model's own confidence leads to well-calibrated expressions of uncertainty, particularly for single-claim answers.

LLM-based data generation for real-world tabular data can be challenged by the lack of sufficient semantic context in feature names used to describe columns. We hypothesize that enriching prompts with domain-specific insights can improve both the quality and efficiency of data generation. To test this hypothesis, we explore three prompt construction protocols: Expert-guided, LLM-guided, and Novel-Mapping. Through empirical studies with the recently proposed GReaT framework, we find that context-enriched prompts lead to significantly improved data generation quality and training efficiency.

Spin- and charge-lattice interactions are potential key factors in the microscopic mechanism of high-temperature superconductivity in cuprates. Although both interactions can dramatically shape the low-energy electronic structure, their phenomenological roles in superconductivity are usually investigated independently. Employing angle-resolved photoemission spectroscopy, we reveal the spectroscopic fingerprint of short-range antiferromagnetic order in conjunction with enhanced electron-phonon interaction in the electron-doped cuprate superconductor $\mathrm{Nd_{1.85}Ce_{0.15}CuO_4}$. The observed mode coupling exhibits a strong momentum dependence that is in striking contrast to the node-antinode dichotomy previously observed in the hole-doped cuprates. Our results reveal an intimate relationship between electron-phonon coupling and antiferromagnetic fluctuations, which collectively sets the stage for unconventional superconductivity in the electron-doped cuprates.

Reliably detecting when a deployed machine learning model is likely to fail on a given input is crucial for ensuring safe operation. In this work, we propose DECIDER (Debiasing Classifiers to Identify Errors Reliably), a novel approach that leverages priors from large language models (LLMs) and vision-language models (VLMs) to detect failures in image classification models. DECIDER utilizes LLMs to specify task-relevant core attributes and constructs a ``debiased'' version of the classifier by aligning its visual features to these core attributes using a VLM, and detects potential failure by measuring disagreement between the original and debiased models. In addition to proactively identifying samples on which the model would fail, DECIDER also provides human-interpretable explanations for failure through a novel attribute-ablation strategy. Through extensive experiments across diverse benchmarks spanning subpopulation shifts (spurious correlations, class imbalance) and covariate shifts (synthetic corruptions, domain shifts), DECIDER consistently achieves state-of-the-art failure detection performance, significantly outperforming baselines in terms of the overall Matthews correlation coefficient as well as failure and success recall. Our codes can be accessed at~\url{https://github.com/kowshikthopalli/DECIDER/}, Comment: Accepted at ECCV (European Conference on Computer Vision) 2024

The escalating battles between attackers and defenders in cybersecurity make it imperative to test and evaluate defense capabilities from the attackers' perspective. However, constructing full-life-cycle cyberattacks and performing red team emulations requires significant time and domain knowledge from security experts. Existing cyberattack simulation frameworks face challenges such as limited technical coverage, inability to conduct full-life-cycle attacks, and the need for manual infrastructure building. These limitations hinder the quality and diversity of the constructed attacks. In this paper, we leveraged the capabilities of Large Language Models (LLMs) in summarizing knowledge from existing attack intelligence and generating executable machine code based on human knowledge. we proposed AURORA, an automatic end-to-end cyberattack construction and emulation framework. AURORA can autonomously build multi-stage cyberattack plans based on Cyber Threat Intelligence (CTI) reports, construct the emulation infrastructures, and execute the attack procedures. We also developed an attack procedure knowledge graph to integrate knowledge about attack techniques throughout the full life cycle of advanced cyberattacks from various sources. We constructed and evaluated more than 20 full-life-cycle cyberattacks based on existing CTI reports. Compared to previous attack simulation frameworks, AURORA can construct multi-step attacks and the infrastructures in several minutes without human intervention. Furthermore, AURORA incorporates a wider range (40% more) of attack techniques into the constructed attacks in a more efficient way than the professional red teams. To benefit further research, we open-sourced the dataset containing the execution files and infrastructures of 20 emulated cyberattacks.

In this work, we investigate the fundamental trade-off regarding accuracy and parameter efficiency in the parameterization of neural network weights using predictor networks. We present a surprising finding that, when recovering the original model accuracy is the sole objective, it can be achieved effectively through the weight reconstruction objective alone. Additionally, we explore the underlying factors for improving weight reconstruction under parameter-efficiency constraints, and propose a novel training scheme that decouples the reconstruction objective from auxiliary objectives such as knowledge distillation that leads to significant improvements compared to state-of-the-art approaches. Finally, these results pave way for more practical scenarios, where one needs to achieve improvements on both model accuracy and predictor network parameter-efficiency simultaneously.

Scaling up neural networks has been a key recipe to the success of large language and vision models. However, in practice, up-scaled models can be disproportionately costly in terms of computations, providing only marginal improvements in performance; for example, EfficientViT-L3-384 achieves <2% improvement on ImageNet-1K accuracy over the base L1-224 model, while requiring $14\times$ more multiply-accumulate operations (MACs). In this paper, we investigate scaling properties of popular families of neural networks for image classification, and find that scaled-up models mostly help with "difficult" samples. Decomposing the samples by difficulty, we develop a simple model-agnostic two-pass Little-Big algorithm that first uses a light-weight "little" model to make predictions of all samples, and only passes the difficult ones for the "big" model to solve. Good little companion achieve drastic MACs reduction for a wide variety of model families and scales. Without loss of accuracy or modification of existing models, our Little-Big models achieve MACs reductions of 76% for EfficientViT-L3-384, 81% for EfficientNet-B7-600, 71% for DeiT3-L-384 on ImageNet-1K. Little-Big also speeds up the InternImage-G-512 model by 62% while achieving 90% ImageNet-1K top-1 accuracy, serving both as a strong baseline and as a simple practical method for large model compression.

Offshore wind farms have emerged as a popular renewable energy source that can generate substantial electric power with a low environmental impact. However, integrating these farms into the grid poses significant complexities. To address these issues, optimal-sized energy storage can provide potential solutions and help improve the reliability, efficiency, and flexibility of the grid. Nevertheless, limited studies have attempted to perform energy storage sizing while including design and operations (i.e., control co-design) for offshore wind farms. As a result, the present work develops a control co-design optimization formulation to optimize multiple objectives and identify Pareto optimal solutions. The graph-based optimization framework is proposed to address the complexity of the system, allowing the optimization problem to be decomposed for large power systems. The IEEE-9 bus system is treated as an onshore AC grid with two offshore wind farms connected via a multi-terminal DC grid for our use case. The developed methodology successfully identifies the Pareto front during the control co-design optimization, enabling decision-makers to select the best compromise solution for multiple objectives.

Anchoring is a recent, architecture-agnostic principle for training deep neural networks that has been shown to significantly improve uncertainty estimation, calibration, and extrapolation capabilities. In this paper, we systematically explore anchoring as a general protocol for training vision models, providing fundamental insights into its training and inference processes and their implications for generalization and safety. Despite its promise, we identify a critical problem in anchored training that can lead to an increased risk of learning undesirable shortcuts, thereby limiting its generalization capabilities. To address this, we introduce a new anchored training protocol that employs a simple regularizer to mitigate this issue and significantly enhances generalization. We empirically evaluate our proposed approach across datasets and architectures of varying scales and complexities, demonstrating substantial performance gains in generalization and safety metrics compared to the standard training protocol.

Altermagnets constitute a novel, third fundamental class of collinear magnetic ordered materials, alongside with ferro- and antiferromagnets. They share with conventional antiferromagnets the feature of a vanishing net magnetization. At the same time they show a spin-splitting of electronic bands, just as in ferromagnets, caused by the atomic exchange interaction. On the other hand, topology has recently revolutionized our understanding of condensed matter physics, introducing new phases of matter classified by intrinsic topological order. Here we connect the worlds of altermagnetism and topology, showing that the electronic structure of the altermagnet CrSb is topological and hosts a novel Weyl semimetallic state. Using high-resolution and spin angleresolved photoemission spectroscopy, we observe a large momentum-dependent spin-splitting in CrSb, reaching up to 1 eV, that induces altermagnetic Weyl nodes with an associated magnetic quantum number. At the surface we observe their spin-polarized topological Fermi-arcs. This establishes that in altermagnets the large energy scale intrinsic to the spin-splitting - orders of magnitude larger than the relativistic spin-orbit coupling - creates its own realm of robust electronic topology., Comment: Main text: 16 pages, 4 figures. Supplementary material: 23 pages, 15 figures. Comments are welcome

A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about the potential of new technologies to offer a personal tutor for every learner and a teaching assistant for every teacher. The full extent of this dream, however, has not yet materialised. We argue that this is primarily due to the difficulties with verbalising pedagogical intuitions into gen AI prompts and the lack of good evaluation practices, reinforced by the challenges in defining excellent pedagogy. Here we present our work collaborating with learners and educators to translate high level principles from learning science into a pragmatic set of seven diverse educational benchmarks, spanning quantitative, qualitative, automatic and human evaluations; and to develop a new set of fine-tuning datasets to improve the pedagogical capabilities of Gemini, introducing LearnLM-Tutor. Our evaluations show that LearnLM-Tutor is consistently preferred over a prompt tuned Gemini by educators and learners on a number of pedagogical dimensions. We hope that this work can serve as a first step towards developing a comprehensive educational evaluation framework, and that this can enable rapid progress within the AI and EdTech communities towards maximising the positive impact of gen AI in education.

Current approaches in Generalized Zero-Shot Learning (GZSL) are built upon base models which consider only a single class attribute vector representation over the entire image. This is an oversimplification of the process of novel category recognition, where different regions of the image may have properties from different seen classes and thus have different predominant attributes. With this in mind, we take a fundamentally different approach: a pre-trained Vision-Language detector (VINVL) sensitive to attribute information is employed to efficiently obtain region features. A learned function maps the region features to region-specific attribute attention used to construct class part prototypes. We conduct experiments on a popular GZSL benchmark consisting of the CUB, SUN, and AWA2 datasets where our proposed Part Prototype Network (PPN) achieves promising results when compared with other popular base models. Corresponding ablation studies and analysis show that our approach is highly practical and has a distinct advantage over global attribute attention when localized proposals are available., Comment: Accepted to the CVPR 2024 LIMIT Workshop

Tunable quantum materials hold great potential for applications. Of special interest are materials in which small lattice strain induces giant electronic responses. The kagome compounds AV3Sb5 (A = K, Rb, Cs) provide a testbed for such singular electronic states. In this study, through angle-resolved photoemission spectroscopy, we provide comprehensive spectroscopic measurements of the giant responses induced by compressive and tensile strains on the charge-density-wave (CDW) order parameter and high-order van Hove singularity (HO-VHS) in CsV3Sb5. We observe a tripling of the CDW gap magnitudes with ~1% strain, accompanied by the changes of both energy and mass of the saddle-point fermions. Our results reveal an anticorrelation between the unconventional CDW order parameter and the mass of a HO-VHS, and highlight the role of the latter in the superconducting pairing. The giant electronic responses uncover a rich strain tunability of the versatile kagome system in studying quantum interplays under lattice perturbations.

Non-Hermitian physics, studying systems described by non-Hermitian Hamiltonians, reveals unique phenomena not present in Hermitian systems. Unlike Hermitian systems, non-Hermitian systems have complex eigenvalues, making their effects less directly observable. Recently, significant efforts have been devoted to incorporating the non-Hermitian effects into condensed matter physics. However, progress has been hindered by the absence of a viable experimental approach. Here, the discovery of surface-selectively spontaneous reconstructed Weyl semimetal NdAlSi provides a feasible experimental platform for studying non-Hermitian physics. Utilizing angle-resolved photoemission spectroscopy measurements, surface-projected density functional theory calculations, and scanning tunneling microscopy measurements, we demonstrate that surface reconstruction in NdAlSi alters surface Fermi arc connectivity and generates new isolated non-topological surface Fermi arcs. In the presence of a magnetic field, the surface-selective spontaneous reconstructed Weyl semimetal NdAlSi can be viewed as a Hermitian bulk--non-Hermitian boundary system. The isolated non-topological surface Fermi arcs on the reconstructed surface act as a loss mechanism and open boundary condition for the topological electrons and bulk states, serving as non-Hermitian boundary states. This discovery provides a good experimental platform for exploring new physical phenomena and potential applications based on boundary non-Hermitian effects, extending beyond purely mathematical concepts., Comment: 21 pages, 4 figures

For several decades, it was widely believed that a non-interacting disordered electronic system could only undergo an Anderson metal-insulator transition due to Anderson localization. However, numerous recent theoretical works have predicted the existence of a disorder-driven non-Anderson phase transition that differ from Anderson localization. The frustration lies in the fact that this non-Anderson disorder-driven transition has not yet been experimentally demonstrated in any system. Here, using angle-resolved photoemission spectroscopy, we present a case study of observing the non-Anderson disorder-driven transition by visualizing the electronic structure of the Weyl semimetal NdAlSi on surfaces with varying amounts of disorder. Our observations reveal that strong disorder can effectively suppress all surface states in the Weyl semimetal NdAlSi, including the topological surface Fermi arcs. This disappearance of surface Fermi arcs is associated with the vanishing of the bulk topological invariant, indicating a quantum phase transition from a Weyl semimetal to a diffusive metal. By analyzing the changes in the electronic structure of NdAlSi, as the surface degrades, we provide a physical picture of this non-Anderson transition from a Weyl semimetal to a diffuse metal. These observations provide the first direct experimental evidence of the non-Anderson disorder-driven transition, a discovery long anticipated by theoretical physicists. The finding dispels longstanding suspicions among researchers that non-Anderson transitions exist in real quantum systems., Comment: 13 pages, 4 figures

Recently, a novel magnetic phase called altermagnetism has been proposed, ushering in a third distinct magnetic phase beyond ferromagnetism and antiferromagnetism. It is expected that this groundbreaking phase exhibits unique physical properties such as C-paired spin-valley locking, anomalous Hall effect, nontrivial Berry phase, and giant magnetoresistance, etc. Among all the predicted candidates, several room temperature altermagnets are suggested to host significant potential applications in the near future. Nevertheless, direct evidence about the spin pattern of the room temperature altermagnet is still unrevealed. Previous studies found that RuO2 is identified as the most promising candidate for room temperature d-wave altermagnetism, exhibiting a substantial spin splitting of up to 1.4 eV. In this study, utilizing angle-resolved photoemission spectroscopy (ARPES), we report experimental observation of the spin splitting in RuO2. Furthermore, employing spin-ARPES, we directly observed the d-wave spin pattern. Our results unequivocally show that RuO2 is a perfect d-wave altermagnet with great potential for upcoming spintronic applications., Comment: 32 pages, 12 figures

It is widely believed that the origin of a significant cause for the voltage and capacity fading observed in lithium (Li)-ion batteries is related to structural modifications occurring in the cathode material during the Li-ion insertion/de-insertion process. The Li-ion insertion/de-insertion mechanism and the resulting structural changes are known to exert a severe strain on the lattice, and consequently leading to performance degradation. Here, with a view to shed more light on the effect of such strain on the structural properties of the cathode material, we have systematically investigated the pressure dependence of structural and transport properties of an LixCoO2 single crystal, grown using 5% excess Li in the precursors. Ambient pressure synchrotron diffraction on these crystals reveals that, the excess Li during the growth, has facilitated the stabilization of a layered rhombohedral phase (space group R3m) as well as a disordered rock-salt phase (space group Fm3m). The volume fraction of the rhombohedral and cubic phase is 60:40, respectively, which remains unchanged up to 10.6 GPa. No structural phase transition has been observed up to 10.6 GPa. An increase in resistance with a decrease in temperature has revealed the semi-metallic nature of the sample. Further, the application of hydrostatic pressure up to 2.8 GPa shows the enhancement of semi-metallic nature. The obtained experimental results can be qualitatively explained via density functional theory (DFT) and thermodynamics modelling. The calculated density of states was reduced, and the activation energy was increased by applied pressure. Our investigations indicate a significant phase stability of the mixed phase crystals under externally applied high pressure and thus suggest the possible use of such mixed phase materials as a cathode in lithium-ion batteries., Comment: 24 pages and 10 figures

While graph neural networks (GNNs) are widely used for node and graph representation learning tasks, the reliability of GNN uncertainty estimates under distribution shifts remains relatively under-explored. Indeed, while post-hoc calibration strategies can be used to improve in-distribution calibration, they need not also improve calibration under distribution shift. However, techniques which produce GNNs with better intrinsic uncertainty estimates are particularly valuable, as they can always be combined with post-hoc strategies later. Therefore, in this work, we propose G-$\Delta$UQ, a novel training framework designed to improve intrinsic GNN uncertainty estimates. Our framework adapts the principle of stochastic data centering to graph data through novel graph anchoring strategies, and is able to support partially stochastic GNNs. While, the prevalent wisdom is that fully stochastic networks are necessary to obtain reliable estimates, we find that the functional diversity induced by our anchoring strategies when sampling hypotheses renders this unnecessary and allows us to support G-$\Delta$UQ on pretrained models. Indeed, through extensive evaluation under covariate, concept and graph size shifts, we show that G-$\Delta$UQ leads to better calibrated GNNs for node and graph classification. Further, it also improves performance on the uncertainty-based tasks of out-of-distribution detection and generalization gap estimation. Overall, our work provides insights into uncertainty estimation for GNNs, and demonstrates the utility of G-$\Delta$UQ in obtaining reliable estimates., Comment: 33 pages; 10 Figures. arXiv admin note: text overlap with arXiv:2309.10976

Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated superior capability to learn complex relationships and often generalize better to new data and problems. This paper presents a novel transformer-powered approach for enhancing prediction accuracy in multi-modal output scenarios, where sparse experimental data is supplemented with simulation data. The proposed approach integrates transformer-based architecture with a novel graph-based hyper-parameter optimization technique. The resulting system not only effectively reduces simulation bias, but also achieves superior prediction accuracy compared to the prior method. We demonstrate the efficacy of our approach on inertial confinement fusion experiments, where only 10 shots of real-world data are available, as well as synthetic versions of these experiments., Comment: MLST

The availability of well-curated datasets has driven the success of Machine Learning (ML) models. Despite greater access to earth observation data in agriculture, there is a scarcity of curated and labelled datasets, which limits the potential of its use in training ML models for remote sensing (RS) in agriculture. To this end, we introduce a first-of-its-kind dataset called SICKLE, which constitutes a time-series of multi-resolution imagery from 3 distinct satellites: Landsat-8, Sentinel-1 and Sentinel-2. Our dataset constitutes multi-spectral, thermal and microwave sensors during January 2018 - March 2021 period. We construct each temporal sequence by considering the cropping practices followed by farmers primarily engaged in paddy cultivation in the Cauvery Delta region of Tamil Nadu, India; and annotate the corresponding imagery with key cropping parameters at multiple resolutions (i.e. 3m, 10m and 30m). Our dataset comprises 2,370 season-wise samples from 388 unique plots, having an average size of 0.38 acres, for classifying 21 crop types across 4 districts in the Delta, which amounts to approximately 209,000 satellite images. Out of the 2,370 samples, 351 paddy samples from 145 plots are annotated with multiple crop parameters; such as the variety of paddy, its growing season and productivity in terms of per-acre yields. Ours is also one among the first studies that consider the growing season activities pertinent to crop phenology (spans sowing, transplanting and harvesting dates) as parameters of interest. We benchmark SICKLE on three tasks: crop type, crop phenology (sowing, transplanting, harvesting), and yield prediction, Comment: Accepted as an oral presentation at WACV 2024

In the present work, a rate-dependent cohesive zone model for the fracture of polymeric interfaces is presented. Inverse calibration of parameters for such complex models through trial and error is computationally tedious due to the large number of parameters and the high computational cost associated. The obtained parameter values are often non-unique and the calibration inherits higher uncertainty when the available experimental data is limited. To alleviate these difficulties, a Bayesian calibration approach is used for the proposed rate-dependent cohesive zone model in this work. The proposed cohesive zone model accounts for both reversible elastic and irreversible rate-dependent separation sliding deformation at the interface. The viscous dissipation due to the irreversible opening at the interface is modeled using elastic-viscoplastic kinematics that incorporates the effects of strain rate. To quantify the uncertainty associated with the inverse parameter estimation, a modular Bayesian approach is employed to calibrate the unknown model parameters, accounting for the parameter uncertainty of the cohesive zone model. Further, to quantify the model uncertainties, such as incorrect assumptions or missing physics, a discrepancy function is introduced and it is approximated as a Gaussian process. The improvement in the model predictions following the introduction of a discrepancy function is demonstrated justifying the need for a discrepancy term. Finally, the overall uncertainty of the model is quantified in a predictive setting and the results are provided as confidence intervals. A sensitivity analysis is also performed to understand the effect of the variability of the inputs on the nature of the output., Comment: To be submitted for peer-review