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Adjoint formulation and constraint handling for gradient-based optimization of compositional reservoir flow

COMPUTATIONAL GEOSCIENCES 2014年第2期18卷 117-137页

作者： Kourounis, Drosos Durlofsky, Louis J. Jansen, Jan Dirk Aziz, Khalid Stanford Univ Dept Energy Resources Engn Stanford CA 94305 USA Delft Univ Technol Dept Geosci & Engn Delft Netherlands

An adjoint formulation for the gradient-based optimization of oil-gas compositional reservoir simulation problems is presented. The method is implemented within an automatic differentiation-based compositional flow simulator (Stanford's Automatic Differentiation-based General Purpose Research Simulator, AD-GPRS). The development of adjoint procedures for general compositional problems is much more challenging than for oil-water problems due to the increased complexity of the code and the underlying physics. The treatment of nonlinear constraints, an example of which is a maximum gas rate specification in injection or production wells, when the control variables are well bottom-hole pressures, poses a particular challenge. Two approaches for handling these constraints are presented-a formal treatment within the optimizer and a simpler heuristic treatment in the forward model. The relationship between discrete and continuous adjoint formulations is also elucidated. Results for four example cases of increasing complexity are presented. Improvements in the objective function (cumulative oil produced) relative to reference solutions range from 4.2 to 11.6 %. The heuristic treatment of nonlinear constraints is shown to offer a cost-effective means for obtaining feasible solutions, which are, in some cases, better than those obtained using the formal constraint handling procedure.

关键词： Adjoint formulation gradient-based optimization Production optimization Recovery optimization Compositional reservoir simulation Discrete adjoint Continuous adjoint Automatic differentiation Nonlinear constraints General constraints

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gradient-based optimization with B-splines on sparse grids for solving forward-dynamics simulations of three-dimensional, continuum-mechanical musculoskeletal system models

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2018年第5期34卷 e2965页

作者： Valentin, J. Sprenger, M. Pflueger, D. Roehrle, O. Univ Stuttgart Inst Parallel & Distributed Syst IPVS Univ Str 38 D-70569 Stuttgart Germany Univ Stuttgart Inst Appl Mech CE Pfaffenwaldring 7 D-70569 Stuttgart Germany Univ Stuttgart Stuttgart Res Ctr Simulat Technol SimTech Pfaffenwaldring 5A D-70569 Stuttgart Germany

Investigating the interplay between muscular activity and motion is the basis to improve our understanding of healthy or diseased musculoskeletal systems. To be able to analyze the musculoskeletal systems, computational models are used. Albeit some severe modeling assumptions, almost all existing musculoskeletal system simulations appeal to multibody simulation frameworks. Although continuum-mechanical musculoskeletal system models can compensate for some of these limitations, they are essentially not considered because of their computational complexity and cost. The proposed framework is the first activation-driven musculoskeletal system model, in which the exerted skeletal muscle forces are computed using 3-dimensional, continuum-mechanical skeletal muscle models and in which muscle activations are determined based on a constraint optimization problem. Numerical feasibility is achieved by computing sparse grid surrogates with hierarchical B-splines, and adaptive sparse grid refinement further reduces the computational effort. The choice of B-splines allows the use of all existing gradient-based optimization techniques without further numerical approximation. This paper demonstrates that the resulting surrogates have low relative errors (less than 0.76%) and can be used within forward simulations that are subject to constraint optimization. To demonstrate this, we set up several different test scenarios in which an upper limb model consisting of the elbow joint, the biceps and triceps brachii, and an external load is subjected to different optimization criteria. Even though this novel method has only been demonstrated for a 2-muscle system, it can easily be extended to musculoskeletal systems with 3 or more muscles.

关键词： B-splines finite element method forward-dynamics simulations gradient-based optimization skeletal muscle mechanics sparse grids

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Constraint handling for gradient-based optimization of compositional reservoir flow

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COMPUTATIONAL GEOSCIENCES 2015年第5期19卷 1109-1122页

作者： Kourounis, Drosos Schenk, Olaf Univ Svizzera Italiana Fac Informat Inst Computat Sci Via Giuseppe Buffi 13 CH-6904 Lugano Switzerland

The development of adjoint procedures for general compositional flow problems is much more challenging than for oil-water problems, due to significantly higher complexity of the underlying physics. The treatment of nondifferentiable constraints, an example of which is a maximum gas rate specification in injection or production wells, when the control variables are well-bottom-hole pressures, poses an additional major challenge. A new formal approach for handling these constraints is presented and compared against a formal treatment within the optimizer employing constraint lumping and a simpler heuristic treatment in the forward model. The three constraint-handling methods are benchmarked for three example cases of increasing complexity. Moreover, the new approach allows the optimizer to converge to optimal solutions exhibiting higher objective values, since unlike the formal lumping-based methods, where a pressure reduction suggested by the optimizer propagates through the smoothing function to all well rates, it handles constraints individually on a per well and per time step basis. The numerical examples show that the new formal constraint-handling approach allows the optimizer to converge significantly faster than formal lumping-based techniques independently

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Comprehensive framework for gradient-based optimization in closed-loop reservoir management

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COMPUTATIONAL GEOSCIENCES 2015年第4期19卷 877-897页

作者： Bukshtynov, Vladislav Volkov, Oleg Durlofsky, Louis J. Aziz, Khalid Stanford Univ Dept Energy Resources Engn Stanford CA 94305 USA

An efficient, robust, and flexible adjoint-based computational framework for performing closed-loop reservoir management is developed and applied. The methodology includes gradient-based production optimization and data assimilation (history matching). Flexibility is achieved through the use of automatic differentiation (AD) within the reservoir simulation, production optimization, and history matching modules. The use of AD will also facilitate the application of closed-loop reservoir management to physical models of higher complexity. A fast sequential convex programming (SCP) solver based on the method of moving asymptotes (MMA) is applied for the production optimization component of the closed-loop. This technique is shown to outperform the sequential quadratic programming (SQP) method, which is commonly used for production optimization computations. The history matching component of the workflow integrates both production data and proxy seismic measurements into a unified adjoint-based data assimilation framework. The effect of noisy data, and data of different types, on the accuracy of the history matching component is assessed. The overall closed-loop reservoir management methodology is tested using the well-documented Brugge model. Results demonstrate the efficient performance of the individual closed-loop components and the improvement in net present value that is achieved using these procedures.

关键词： Closed-loop reservoir management Production optimization History matching Seismic data Noisy data gradient-based optimization Adjoint formulation Automatic differentiation PCA-based parameterization

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EFFICIENT INVERSE DESIGN OF ACOUSTIC METAMATERIALS USING gradient-based optimization

EFFICIENT INVERSE DESIGN OF ACOUSTIC METAMATERIALS USING GRA...

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ASME International Mechanical Engineering Congress and Exposition (IMECE)

作者： Gerges, Samer Amirkulova, Feruza A. Samaniego, Jovana San Jose State Univ San Jose CA 95192 USA

ISBN: (纸本)9780791887615

This work presents an efficient implementation of the inverse design of acoustic metamaterials using the gradient-based optimization (GBO) method. The inverse design is based on an optimization process using the semi-analytical optimization approach by adjusting the radius and position of each scatterer in the non-uniform planar configuration of scatterers of various radii. As an illustration of the method, the application of the GBO method for the design of an acoustic lens is demonstrated. The proposed inverse design method is implemented utilizing the GBO algorithms, such as the sequential quadratic programming (SQP) algorithms and the Julia programming language, which is suitable for large-scale simulations and optimization problems. Jointly they create a synergy that enhances the optimization process. The results are compared against computations performed on MATLAB using the fmincon solver with the SQP algorithms. This paper will present numerical examples of sound localization using random and symmetric non-uniform planar configurations of cylindrical rigid scatterers submerged in water.

关键词： metamaterials acoustic lens gradient-based optimization efficient inverse design Julia programming language

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LeapAttack: Hard-Label Adversarial Attack on Text via gradient-based optimization 22

LeapAttack: Hard-Label Adversarial Attack on Text via Gradie...

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28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KKD)

作者： Ye, Muchao Chen, Jinghui Miao, Chenglin Wang, Ting Ma, Fenglong Penn State Univ University Pk PA 16802 USA Univ Georgia Athens GA 30602 USA

ISBN: (纸本)9781450393850

Generating text adversarial examples in the hard-label setting is a more realistic and challenging black-box adversarial attack problem, whose challenge comes from the fact that gradient cannot be directly calculated from discrete word replacements. Consequently, the effectiveness of gradient-based methods for this problem still awaits improvement. In this paper, we propose a gradient-based optimization method named LeapAttack to craft high-quality text adversarial examples in the hard-label setting. To specify, LeapAttack employs the word embedding space to characterize the semantic deviation between the two words of each perturbed substitution by their difference vector. Facilitated by this expression, LeapAttack gradually updates the perturbation direction and constructs adversarial examples in an iterative round trip: firstly, the gradient is estimated by transforming randomly sampled word candidates to continuous difference vectors after moving the current adversarial example near the decision boundary;secondly, the estimated gradient is mapped back to a new substitution word based on the cosine similarity metric. Extensive experimental results show that in the general case LeapAttack can efficiently generate high-quality text adversarial examples with the highest semantic similarity and the lowest perturbation rate in the hard-label setting.

关键词： hard-label text adversarial attack gradient-based optimization

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NRAgo: Solving SMT(NRA) Formulas with gradient-based optimization 38

NRAgo: Solving SMT(NRA) Formulas with Gradient-based Optimiz...

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38th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Liu, Minghao Lv, Kunhang Huang, Pei Han, Rui Jia, Fuqi Zhang, Yu Ma, Feifei Zhang, Jian Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Stanford Univ Stanford CA USA Chinese Acad Sci Inst Software Lab Parallel Software & Comp Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350329964

The satisfiability problem modulo the nonlinear real arithmetic (NRA) theory serves as the foundation for a wide range of important applications, such as model checking, program analysis, and software testing. However, due to the high computational complexity, developing efficient solving algorithms for this problem has consistently presented a substantial challenge. We present a hybrid SMT(NRA) solver, called NRAgo, which combines the efficiency of gradient-based optimization method with the completeness of algebraic solving algorithm. With our approach, the practical performance on many satisfiable instances is substantially improved. The experimental evaluation shows that NRAgo achieves remarkable acceleration effects on a set of challenging SMT(NRA) benchmarks that are hard to solve for state-of-the-art SMT solvers.

关键词： satisfiability modulo theories nonlinear real arithmetic gradient-based optimization

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optimization of Microbial Fuel Cells: Enhancing Electrical Output Through gradient-based optimization 6

Optimization of Microbial Fuel Cells: Enhancing Electrical O...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Mehmood, Hassan Zafar, Tayyab Kamal, Khurram Aun Rizvi, Syed Muhammad Ratlamwala, Tahir National University of Sciences and Technology Department of Mechatronics Engineering Islamabad Pakistan Department of Engineering Science Islamabad Pakistan

ISBN: (纸本)9798350355703

Microbial Fuel Cells (MFCs) are environment-friendly devices to generate energy through microbial metabolism of the chemical energy of organic compounds. Few of the critical challenges of implementing MFCs are their low power density, high internal resistance, and poor electron transport. These factors are major drawbacks to the MFCs efficiency and ability to scale up MFCs. The proposed work aims to enhance the MFC performance by optimizing the proportions of key operational parameters to overcome the implementation challenges. These parameters include Substrate Concentration (S), Anodophilic Biomass (xa), Methanogenic Biomass (x/m), and Oxidized Mediator (M/ox). The parameters are optimized using a gradient based optimization (GBO). During the optimization process, it was imperative to establish a sound mathematical model that predicted MFC's biological and physical behavior. With the implementation of the GBO algorithm, the output current is enhanced by 8. 89 percent. The results shows that promising future of the proposed method. © 2024 IEEE.

关键词： Anodophilic Biomass Bioelectrochemical Systems gradient-based optimization Methanogenic Biomass Microbial Fuel Cells Oxidized Mediator Substrate Concentration Sustainable Energy

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Broadband acoustic lens design using gradient-based optimization 24

Broadband acoustic lens design using gradient-based optimiza...

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24th International Congress on Acoustics, ICA 2022

作者： Amirkulova, Feruza Gerges, Samer Norris, Andrew N. Mechanical Engineering Department San José State University San José95192 United States Mechanical and Aerospace Engineering Department Rutgers University Piscataway08854 United States

In this work, we present a gradient-based optimization (GBO) method [1, 2] for the design of a broadband acoustic lens. This design is based on an optimization process using the semi-analytical optimization approach by means of multiple scattering theory. The GBO algorithm maximizes the sound amplification at the focal point by evaluating pressure derivative with respect to the cylinder positions and then perturbatively optimizing the position of each cylinder in the lens. The results of the GBO of the uni- and multi-directional broadband acoustic lens designs are presented including performance measures for the frequency and the incidence angle dependency. Examples of sound localization and focusing effects produced by planar nonuniform and uniform configurations of cylindrical structures such as rigid or void cylinders as well as thin elastic shells are shown. © ICA *** rights reserved

关键词： Acoustic metamaterials gradient-based optimization Sound localization

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Descriptor-based reconstruction of three-dimensional microstructures through gradient-based optimization

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ACTA MATERIALIA 2022年 227卷

作者： Seibert, Paul Rassloff, Alexander Ambati, Marreddy Kaestner, Markus Tech Univ Dresden Inst Solid Mech D-01069 Dresden Germany Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA Tech Univ Dresden Dresden Ctr Computat Mat Sci Dresden Germany Tech Univ Dresden Dresden Ctr Fatigue & Reliabil Dresden Germany

Microstructure reconstruction is an important cornerstone to the inverse materials design concept. In this work, a general algorithm is developed to reconstruct a three-dimensional microstructure from given descriptors. based on two-dimensional (2D) micrographs, this reconstruction algorithm allows valuable insight through spatial visualization of the microstructure and in silico studies of structure-property linkages. The formulation ensures computational efficiency by casting microstructure reconstruction as a gradient-based optimization problem. Herein, the descriptors can be chosen freely, such as spatial correlations or Gram matrices, as long as they are differentiable with respect to the microstructure. Because real microstructure samples are commonly available as 2D microscopy images only, the desired descriptors for the reconstruction process are prescribed on orthogonal 2D slices. This adds a source of noise, which is handled in a new, superior and interpretable manner. The efficiency and applicability of this formulation is demonstrated by various numerical experiments.(c) 2022 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

关键词： Microstructure Reconstruction 3D Characterization Statistics gradient-based optimization

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