Heterogeneous Material Modeling with Distance Fields

We propose a universal approach to the outstanding problem of computer modeling of continuously varying distributions of material properties satisfying prescribed material quantities and rates on a finite collection of geometric features. The central notion is a parameterization of the shape’s interior by distances from the material features - either exactly or approximately; this parameterization supports specification, interpolation, and optimization of desired material distributions in a systematic and controlled fashion. We demonstrate how the approach can be implemented within the existing framework of solid modeling and its numerous advantages, including: • precise and intuitive control using explicit, analytic, differential, and integral constraints specified on the original (not discretized) geometric model; • applicability to material features of arbitrary dimension, shape, and topology; and • guaranteed smoothness and analytic properties for superior performance, analysis and optimization. Last, but not least, the proposed approach subsumes and generalizes a number of other proposals for heterogeneous material modeling for FGM, heterogeneous solid modeling, and solid free-form fabrication.Mechanical Engineerin

Hybrid Statistical and Engineering Optimization Architectures in Early Multidisciplinary Designs of Resilience and Expensive Black-box Complex Systems

Practical engineering design problems are generally multi-disciplinary with limited budget and high risk in terms of life loss, economic resources, etc. In the early phase of such problems, selection of true efficient designs is desired while minimizing overall design cost by avoiding expensive search processes. However, the task is difficult for a simple optimization framework due to the formulation complexity, high function evaluation cost, uncertain design parameters etc. Thus, the overall research goal is to develop complex, hybrid optimization architectures for solving early design problems considering the trade-off among model complexity, performance and cost. We start by comparing multiple architectures, and investigated a nested bi-level architecture for early resilience design with discrete design space and with a trade-off among multiple objectives at different risk level scenarios. The work then focused on increased problem complexity with black-box functions in a mechanical design classification problem with discontinuous design space using a sequential Bayesian Optimization (BO) architecture to locate an unknown creep-fatigue failure constraint boundary. The work then extends a weighted Tchebycheff black-box multi-objective BO (MO-BO) architecture for mechanical design with a trade-off between design risk and cost, with model calibration through regression analysis of unknown parameters. Finally, we investigate an iterative regression model selection procedure, nested into the proposed MO-BO, to enhance design flexibility, estimation and overall performance. This work can be applicable to any domains of complex or/and expensive black-box system design problems

Bi-level flexible-robust optimization for energy allocation problems

A common problem in energy allocation problems is managing the trade-off between selling surplus energy to maximize short term revenue, versus holding surplus energy to hedge against future shortfalls. For energy allocation problems, this surplus represents resource flexibility and is quantified as the surplus energy after meeting the demand. The decision maker has an option to sell or hold the flexibility for future use. As a decision in the current period can affect future decisions significantly, future risk evaluation of negative shocks (or uncertainties) is recommended for the current decision in which a traditional robust optimization is not efficient. Therefore, an approach to Flexible-Robust Optimization has been formulated by integrating a Real Options Model with the Robust Optimization framework. Real options analysis is an efficient economic model for risk evaluation in investment problems. In the energy problem, the real options model evaluates the future risk, and provides the value of holding flexibility, whereas the robust optimization quantifies uncertainty and provide a robust solution (i.e. a solution which is generally insensitive to uncertainties) of net revenue by selling flexibility. This integration or models has introduced compatibility issues which have been discussed extensively in the literature. However, the limitations have been overcome successfully by implementing bi-level programming in this work. Therefore, a complete general mathematical formulation of Bi-Level Flexible-Robust Optimization model is presented and results shown to provide an efficient decision making process in energy sectors.Keywords: flexible-robust optimization, energy allocation, bi-level optimization, real option

Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis

Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as EELS or 4D STEM, that contain information on a wide range of structural, physical, and chemical properties of materials. To extract valuable insights from these data, it is crucial to identify physically separate regions in the data, such as phases, ferroic variants, and boundaries between them. In order to derive an easily interpretable feature analysis, combining with well-defined boundaries in a principled and unsupervised manner, here we present a physics augmented machine learning method which combines the capability of Variational Autoencoders to disentangle factors of variability within the data and the physics driven loss function that seeks to minimize the total length of the discontinuities in images corresponding to latent representations. Our method is applied to various materials, including NiO-LSMO, BiFeO3, and graphene. The results demonstrate the effectiveness of our approach in extracting meaningful information from large volumes of imaging data. The fully notebook containing implementation of the code and analysis workflow is available at https://github.com/arpanbiswas52/PaperNotebooksComment: 20 pages, 7 figures in main text, 4 figures in Supp Ma

Cerebrospinal fluid adenosine deaminase level as a diagnostic marker in adult tuberculous meningitis: a study conducted in a tertiary care hospital of Eastern India

Background: Tubercular meningitis is one of the highly prevalent form of meningitis in the world and is a significant cause of morbidity and mortality in developing countries like India. Lack of early and timely diagnosis and subsequent initiation of treatment makes the fatality rate even higher. Cerebrospinal fluid (CSF) analysis is most important aspect of lab diagnosis in tuberculous meningitis (TBM) worldwide. The objective of this study was to study the cerebrospinal fluid CSF adenosine deaminase (ADA) levels in TBM and non-TBM meningitis cases and to determine its diagnostic significance as a biochemical marker of TBM infection.Methods: The study population comprised three different patient groups. TBM (n=36), pyogenic meningitis (n=17) and aseptic meningitis group (n=12). Total 75 subjects were enrolled consecutively in the study and CSF specimens were collected from them. ADA and other cytological and biochemical estimation were carried out using standard protocol.Results: ADA level in TBM in compare to non-TBM was more and mean ADA level of TBM, AM and PM are 26.423±3.8, 2.602±0.5 and 6.29±0.3 respectively. There are highly significant differences between the TBM and non-TBM groups and also in compare with individual groups.Conclusions: CSF ADA levels are elevated in the TBM cases as compared to the non-TBM - meningitis cases.  Results are statistically significant. It is a simple and inexpensive diagnostic adjunctive test in the rapid and early diagnosis of TBM

Autonomous convergence of STM control parameters using Bayesian Optimization

Scanning Tunneling microscopy (STM) is a widely used tool for atomic imaging of novel materials and its surface energetics. However, the optimization of the imaging conditions is a tedious process due to the extremely sensitive tip-surface interaction, and thus limits the throughput efficiency. Here we deploy a machine learning (ML) based framework to achieve optimal-atomically resolved imaging conditions in real time. The experimental workflow leverages Bayesian optimization (BO) method to rapidly improve the image quality, defined by the peak intensity in the Fourier space. The outcome of the BO prediction is incorporated into the microscope controls, i.e., the current setpoint and the tip bias, to dynamically improve the STM scan conditions. We present strategies to either selectively explore or exploit across the parameter space. As a result, suitable policies are developed for autonomous convergence of the control-parameters. The ML-based framework serves as a general workflow methodology across a wide range of materials.Comment: 31 pages, 5 figures and Supplementary Informatio

Boost invariant spin hydrodynamics within the first order in derivative expansion

Boost-invariant equations of spin hydrodynamics confined to the first-order terms in gradients are numerically solved. The spin equation of state, relating the spin density tensor to the spin chemical potential, is consistently included in the first order. Depending on its form and the structure of the spin transport coefficients, we find solutions which are both stable and unstable within the considered evolution times of 10 fm/c. These findings are complementary to the recent identification of stable and unstable modes for perturbed uniform spin systems described by similar hydrodynamic frameworks.Comment: 11 pages, 3 figures. Comments are welcom

WIMPs in Dilatonic Einstein Gauss-Bonnet Cosmology

We use the Weakly Interacting Massive Particle (WIMP) thermal decoupling scenario to probe Cosmologies in dilatonic Einstein Gauss-Bonnet (dEGB) gravity, where the Gauss-Bonnet term is non-minimally coupled to a scalar field with vanishing potential. We put constraints on the model parameters when the ensuing modified cosmological scenario drives the WIMP annihilation cross section beyond the present bounds from DM indirect detection searches. In our analysis we assumed WIMPs that annihilate to Standard Model particles through an s-wave process. For the class of solutions that comply with WIMP indirect detection bounds, we find that dEGB typically plays a mitigating role on the scalar field dynamics at high temperature, slowing down the speed of its evolution and reducing the enhancement of the Hubble constant compared to its standard value. For such solutions, we observe that the corresponding boundary conditions at high temperature correspond asymptotically to a vanishing deceleration parameter q, so that the effect of dEGB is to add an accelerating term that exactly cancels the deceleration predicted by General Relativity. The bounds from WIMP indirect detection are nicely complementary to late-time constraints from compact binary mergers. This suggest that it could be interesting to use other Early Cosmology processes to probe the dEGB scenario.Comment: 30 pages, 8 figures, 1 tabl