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An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions
Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes' rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle these challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.
Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients
We present a novel approach for solving steady-state stochastic partial differential equations in high-dimensional random parameter space. The proposed approach combines spatial domain decomposition with basis adaptation for each subdomain. The basis adaptation is used to address the curse of dimensionality by constructing an accurate low-dimensional representation of the stochastic PDE solution (probability density function and/or its leading statistical moments) in each subdomain. Restricting the basis adaptation to a specific subdomain affords finding a locally accurate solution. Then, the solutions from all of the subdomains are stitched together to provide a global solution. We support our construction with numerical experiments for a steady-state diffusion equation with a random spatially dependent coefficient. Our results show that accurate global solutions can be obtained with significantly reduced computational costs.
Adaptive Model Predictive Control for Real-Time Dispatch of Energy Storage Systems.
Abstract not provided.
Mouse models of Ebola virus tolerance and lethality: characterization of CD-1 mice infected with wild-type, guinea pig-adapted, or mouse-adapted virus
Development of lethal models of Ebola virus disease has been achieved by the serial passage of virus isolates from human cases in mice and guinea pigs. Use of mice infected with non-adapted virus has been limited due to the absence of overt clinical disease. In recent years, newly recognized sequelae identified in human cases has highlighted the importance of continued investigations of non-lethal infection both in humans and animal models. Here, we revisit the use of rodent-adapted and non-adapted Ebola virus (EBOV) in mice to investigate infection tolerance and future utility of these models in pathogenesis and therapeutic intervention studies. We found that like non-adapted wild-type EBOV, guinea pig-adapted EBOV resulted in widespread tissue infection, variably associated with tissue pathology, and alterations in clinical and immunological analytes in the absence of overt disease. Notably, infection with either non-lethal variant did not greatly differ from lethal mouse-adapted EBOV until near the time end-point criteria are reached in these mice. Furthermore, these data support future investigations of pathogenesis, convalescence, and sequelae in mouse models of virus tolerance.
Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
TETRIS-ADAPT-VQE: An adaptive algorithm that yields shallower, denser circuit Ansätze
Adaptive quantum variational algorithms are particularly promising for simulating strongly correlated systems on near-term quantum hardware, but they are not yet viable due, in large part, to the severe coherence time limitations on current devices. In this paper, we introduce an algorithm called TETRIS-ADAPT-VQE (tiling efficient trial circuits with rotations implemented simultaneously adaptive derivative-assembled problem-tailored variational quantum eigensolver), which iteratively builds up variational a few operators at a time in a way dictated by the problem being simulated. This algorithm is a modified version of the ADAPT-VQE algorithm, in which the one-operator-at-a-time rule is lifted to allow for the addition of multiple operators with disjoint supports in each iteration. TETRIS-ADAPT-VQE results in denser but significantly shallower circuits, without increasing the number of controlled- gates or variational parameters. Its advantage over the original algorithm in terms of circuit depths increases with the system size. Moreover, the expensive step of measuring the energy gradient with respect to each candidate unitary at each iteration is performed only a fraction of the time compared with ADAPT-VQE. These improvements bring us closer to the goal of demonstrating a practical quantum advantage on quantum hardware. Published by the American Physical Society 2024
SSD-optimized workload placement with adaptive learning and classification in HPC environments
In recent years, non-volatile memory devices such as SSD drives have emerged as a viable storage solution due to their increasing capacity and decreasing cost. Due to the unique capability and capacity requirements in large scale HPC (High Performance Computing) storage environment, a hybrid configuration (SSD and HDD) may represent one of the most available and balanced solutions considering the cost and performance. Under this setting, effective data placement as well as movement with controlled overhead become a pressing challenge. In this paper, we propose an integrated object placement and movement framework and adaptive learning algorithms to address these issues. Specifically, we present a method that shuffle data objects across storage tiers to optimize the data access performance. The method also integrates an adaptive learning algorithm where realtime classification is employed to predict the popularity of data object accesses, so that they can be placed on, or migrate between SSD or HDD drives in the most efficient manner. We discuss preliminary results based on this approach using a simulator we developed to show that the proposed methods can dynamically adapt storage placements and access pattern as workloads evolve to achieve the best system level performance such as throughput.
Adaptive EAGLE dynamic solution adaptation and grid quality enhancement
In the effort described here, the elliptic grid generation procedure in the EAGLE grid code was separated from the main code into a subroutine, and a new subroutine which evaluates several grid quality measures at each grid point was added. The elliptic grid routine can now be called, either by a computational fluid dynamics (CFD) code to generate a new adaptive grid based on flow variables and quality measures through multiple adaptation, or by the EAGLE main code to generate a grid based on quality measure variables through static adaptation. Arrays of flow variables can be read into the EAGLE grid code for use in static adaptation as well. These major changes in the EAGLE adaptive grid system make it easier to convert any CFD code that operates on a block-structured grid (or single-block grid) into a multiple adaptive code.
Genetic basis of photosynthetic responses to cold in two locally adapted populations of Arabidopsis thaliana
Local adaptation is common, but the traits and genes involved are often unknown. Physiological responses to cold probably contribute to local adaptation in wide-ranging species, but the genetic basis underlying natural variation in these traits has rarely been studied. Using a recombinant inbred (495 lines) mapping population from locally adapted populations of Arabidopsis thaliana from Sweden and Italy, we grew plants at low temperature and mapped quantitative trait loci (QTLs) for traits related to photosynthesis: maximal quantum efficiency (F v /F m ), rapidly reversible photoprotection (NPQ fast ), and photoinhibition of PSII (NPQ slow ) using high-throughput, whole-plant measures of chlorophyll fluorescence. In response to cold, the Swedish line had greater values for all traits, and for every trait, large effect QTLs contributed to parental differences. We found one major QTL affecting all traits, as well as unique major QTLs for each trait. Six trait QTLs overlapped with previously published locally adaptive QTLs based on fitness measured in the native environments over 3 years. Our results demonstrate that photosynthetic responses to cold can vary dramatically within a species, and may predominantly be caused by a few QTLs of large effect. Some photosynthesis traits and QTLs probably contribute to local adaptation in this system.
Climate adaptation in Populus trichocarpa : key adaptive loci identified for stomata and leaf traits
We investigated adaptive genetic variation in Populus trichocarpa, a potential biofuel feedstock crop, to better understand how physiological traits may influence tolerance to water limitation. Our study focused on leaf and stomatal traits, given their roles in plant–water relations and adaptation. Using a diversity panel of over 1300 genotypes, we measured 14 leaf and stomatal traits under control (well-watered) and drought (water-limited) conditions. We conducted genome-wide association studies (GWAS), climate association analyses, and transcriptome (RNA-seq) profiling to identify genetic loci associated with phenotypic variation and adaptation. Stomatal traits, including size and density, were correlated with the climate of origin, with genotypes from more arid regions tending to have smaller but denser stomata. GWAS identified multiple loci associated with trait variation, including a major-effect region on chromosome 10 linked to stomatal size and abaxial contact angle. This locus overlapped with a tandem array of 3-ketoacyl-CoA synthase (KCS) genes and showed strong allele–climate and gene expression associations. Our findings reveal genetic and phenotypic variation consistent with local adaptation and suggest that future climates may favor alleles associated with smaller stomata, particularly under increasing aridity. This work provides insights into climate adaptation and breeding strategies for resilience in perennial crops.
Adaptive quantum/molecular mechanics: what have we learned, where are we, and where do we go from here?: Adaptive QM/MM
Adaptive quantum-mechanics/molecular-mechanics (QM/MM) methods feature on-the-fly reclassification of atoms as QM or MM during a molecular dynamics(MD) simulation, allowing the location and contents of the QM subsystem to be dynamically updated as needed. Such flexibility is a distinct advantage over conventional QM/MM, where a ‘static’ boundary is retained between the QM and MM subsystems. The ‘dynamic’ boundary in adaptive QM/MM allows a finite-size QM to sustain simulations with an arbitrary length of time. Here, to ensure smooth transitions between QM and MM, the energy or forces are interpolated.Special treatments are applied so that artifacts are eliminated or minimized.Recent developments have shed light on the relationship between the adaptive algorithms that describe Hamiltonian and non-Hamiltonian systems. Originally developed to model an ion solvated in bulk solvent, adaptive QM/MM has been enhanced in many aspects, including the treatment of molecular fragments in macromolecules, monitoring molecules entering/leaving binding sites, and tracking proton transfer via the Grotthuss mechanism. Because the size of the QM region can be set as small as possible in adaptive QM/MM, the computational costs can be kept low. Small QM subsystems also facilitate the utilization of high-level QM theory and long simulation time, which can potentially lead to new insights.
Dose Escalation for Locally Advanced Lung Cancer Using Adaptive Radiation Therapy With Simultaneous Integrated Volume-Adapted Boost
Purpose: To test the feasibility of a planned phase 1 study of image-guided adaptive radiation therapy in locally advanced lung cancer. Methods and Materials: Weekly 4-dimensional fan beam computed tomographs (4D FBCT) of 10 lung cancer patients undergoing concurrent chemoradiation therapy were used to simulate adaptive radiation therapy: After an initial intensity modulated radiation therapy plan (0-30 Gy/2 Gy), adaptive replanning was performed on week 2 (30-50 Gy/2 Gy) and week 4 scans (50-66 Gy/2 Gy) to adjust for volume and shape changes of primary tumors and lymph nodes. Week 2 and 4 clinical target volumes (CTV) were deformably warped from the initial planning scan to adjust for anatomical changes. On the week 4 scan, a simultaneous integrated volume-adapted boost was created to the shrunken primary tumor with dose increases in 5 0.4-Gy steps from 66 Gy to 82 Gy in 2 scenarios: plan A, lung isotoxicity; plan B, normal tissue tolerance. Cumulative dose was assessed by deformably mapping and accumulating biologically equivalent dose normalized to 2 Gy-fractions (EQD2). Results: The 82-Gy level was achieved in 1 in 10 patients in scenario A, resulting in a 13.4-Gy EQD2 increase and a 22.1% increase in tumor control probability (TCP) compared to the 66-Gy plan. In scenario B, 2 patients reached the 82-Gy level with a 13.9 Gy EQD2 and 23.4% TCP increase. Conclusions: The tested image-guided adaptive radiation therapy strategy enabled relevant increases in EQD2 and TCP. Normal tissue was often dose limiting, indicating a need to modify the present study design before clinical implementation.
Adapter Python IO (Adapter) v1.0
The Adapter Python IO software, in short Adapter or the Adapter software, encapsulates certain Python IO capabilities used for loading in and writing out data when performing analytical Python code runs. More specificcally, it provides a Python API to load data tables from various formats such as XLSX (MS Excel), CSV, and database, into Python code as Pandas DataFrames, as well as to write out tables into a database or CSV files. The Adapter software standardizes a way to point the code to one or multiple input files of one or multiple formats. Therefore, its main feature is the ability to convert data tables identified in one main and, optionally, one or more additional input files, into database tables and Pandas DataFrames for downstream usage in any compatible software. In addition to the loading capability, an instance of the Adapter IO object has the capability to write data out. If the write capability is invoked, all loaded tables are written as either a single database or a set of CSV files, or both, to a location specified in the dedicated input table.
An Adaptive Geometry-Free Thermo-Mechanical Model for Directed Energy Deposition Process Modeling
This presentation describes a novel, geometry-free thermo-mechanical model with adaptive subdomain con- struction to accurately predict the thermal conditions, distortions, and residual stresses throughout the directed energy deposition (DED) process. A novel finite element workflow is designed to con- duct the numerical analysis, based on the multi-app and data transfer capabilities in the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE). Unlike with traditional methods, the part geometry in this model is not predefined. Instead, it is a combined effect of the processing parameters and material properties. At each time step, the model utilizes a subdomain construction paradigm to model the material deposition. A specialized mesh adaptivity scheme is incorporated to provide an accurate prediction while reducing the overall computational cost. The results generated by the proposed model show general agreement with the experimental measurements for the single track scan with varying processing parameters and demonstrate reasonable predictions for higher material buildups.
A Moving Embedded Boundary Approach for the Compressible Navier-Stokes Equations in a Block-Structured Adaptive Refinement Framework
A computational technique has been developed to perform compressible flow simulations involving moving boundaries using an embedded boundary approach within the block-structured adaptive mesh refinement (SAMR) framework of AMReX [1], [91], [92]. We leverage the SAMR capability to obtain quantitatively accurate results whilst using robust, second-order finite volume schemes. A conservative, unsplit, cut-cell approach is utilized and a ghost-cell approach is developed for computing the flux on the moving, embedded boundary faces. A third-order least-squares formulation has been developed to compute the wall velocity gradients, and was found to significantly improve the performance of the solver in terms of the quantitative comparison of surface quantities such as the skin friction coefficient. Various test cases are performed to validate the method, and compared with analytical, experimental, and other numerical results in literature. Inviscid and viscous test cases are performed that span a wide regime of flow speeds - acoustic (harmonically pulsating sphere), smooth flows (expansion fan created by a receding piston) and flows with shocks (shock-cylinder interaction, shock-wedge interaction, pitching NACA 0012 airfoil and shock-cone interaction). A closed system with moving boundaries - an oscillating piston in a cylinder, showed that the percentage error in mass within the system decreases with refinement, demonstrating that the numerical scheme is conservative with grid refinement, but is not discretely conservative. Viscous test cases involve that of a horizontally moving cylinder at Re = 40, an inline oscillating cylinder at Re = 100, and a transversely oscillating cylinder at Re = 185. The judicious use of adaptive mesh refinement with appropriate refinement criteria to capture the regions of interest leads to well-resolved flow features, and good quantitative comparison is observed with the results available in literature.
A novel approach for adaptive skeleton toolpath generation
Industry 4.0 is revolutionizing manufacturing through the integration of automation and real-time data sharing in cyber-physical systems. At the forefront of this revolution is large-format additive manufacturing. In large-format printing, parts are often designed to be an even number of beads wide to produce a completely dense part. However, voids can still arise. This is often due to the part not being an even number of bead widths wide in some areas, or in geometry containing acute angles, as the process of generating closed contours cannot completely fill the space. Voids can be tolerated in smaller models, but in large-format additive manufacturing they may cause mechanical defects. To fill these voids, open loop paths called skeletons are often used, but they are typically limited by the physical constraints defined in the slicing software. To address this, researchers at Oak Ridge National Laboratory have extended skeleton toolpaths via an adaptive methodology. These adaptive skeletons were found to better fill void spaces through manipulation of physical parameters of the build process and were calculated as part of the slicing process.
Real-Time Model-Adaptive Relaying Applied to Microgrid Protection
In microgrids, the short-circuit current magnitude is significantly limited by more than an order of magnitude due to the relatively small inverter-based resources. Commercially available protective devices for distribution cannot reliably protect a microgrid due to their dependence on the magnitude of the fault current. Moreover, overcurrent relays typically cannot function properly for a microgrid because they are incapable of detecting faults and/or performing the coordination between the relays in inverter-based microgrids operated in the islanded mode. This paper proposes a model-adaptive relay designed to adjust the relay curves based on the available generation and the network topology. The proposed method runs a real-time model of the microgrid, which gathers information from the network to calculate the available short-circuit current in the specified node. The fault current from the model is then used for the adaptive algorithm to calculate the relay settings, considering coordination with the downstream fuses and upstream reclosers. This work presents the validation of the proposed method in Hardware-in-the-Loop, in a hardware testbed as well as field deployed in a real microgrid in East-Tennessee.