A novel design optimization approach (ActivO) that employs an ensemble of machine learning algorithms is presented. The proposed approach is a surrogate-based scheme, where the predictions of a weak leaner and a strong learner are utilized within an active learning loop. The weak learner is used to identify promising regions within the design space to explore, while the strong learner is used to determine the exact location of the optimum within promising regions. For each design iteration, exploration is done by randomly selecting evaluation points within regions where the weak learner-predicted fitness is high. The global optimum obtained by using the strong learner as a surrogate is also evaluated to enable rapid convergence once the most promising region has been identified. First, the performance of ActivO was compared against five other optimizers on a cosine mixture function with 25 local optima and one global optimum. In the second problem, the objective was to minimize indicated specific fuel consumption of a compression-ignition internal combustion (IC) engine while adhering to desired constraints associated with in-cylinder pressure and emissions. Here, the efficacy of the proposed approach is compared to that of a genetic algorithm, which is widely used within the internal combustion engine community for engine optimization, showing that ActivO reduces the number of function evaluations needed to reach the global optimum, and thereby time-to-design by 80%. Furthermore, the optimization of engine design parameters leads to savings of around 1.9% in energy consumption, while maintaining operability and acceptable pollutant emissions.
Tecgraf Institute, PUC-Rio（里约热内卢天主教大学 Tecgraf 研究所）；Department of Mechanical Engineering, PUC-Rio（里约热内卢天主教大学机械工程系）
A common workflow for many engineering design problems requires the evaluation of the design system to be investigated under a range of conditions. These conditions usually involve a combination of several parameters. To perform a complete evaluation of a single candidate configuration, it may be necessary to perform hundreds to thousands of simulations. This can be computationally very expensive, particularly if several configurations need to be evaluated, as in the case of the mathematical optimization of a design problem. Although the simulations are extremely complex, generally, there is a…
Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design
Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes. Traditional PDE solvers are very accurate but computationally costly. On the other hand, Machine Learning (ML) methods offer a significant computational speedup but face challenges with accuracy and generalization to different PDE conditions, such as geometry, boundary conditions, initial conditions and PDE source terms. In this work, we propose a novel ML-based approach, CoAE-MLSim (Composable AutoEncoder Machine Learning Simulation), which is an unsupervised, low…
University of Luxembourg（卢森堡大学）；Institute of Fundamental Technological Research, Polish Academy of Sciences（波兰科学院基础技术研究所）
Deep learning surrogate models are being increasingly used in accelerating scientific simulations as a replacement for costly conventional numerical techniques. However, their use remains a significant challenge when dealing with real-world complex examples. In this work, we demonstrate three types of neural network architectures for efficient learning of highly non-linear deformations of solid bodies. The first two architectures are based on the recently proposed CNN U-NET and MAgNET (graph U-NET) frameworks which have shown promising performance for learning on mesh-based data. The third arc…
Accelerating scientific discovery with the common task framework
AI Institute in Dynamic Systems（动态系统人工智能研究所）；Department of Applied Mathematics and Electrical and Computer Engineering（应用数学与电气计算机工程系）；Google DeepMind（谷歌DeepMind）；School of Engineering and Applied Physics（工程与应用物理学院）；Meta Platforms, Inc.（Meta平台公司）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Flatiron Institute（Flatiron研究所）；Google Research（谷歌研究）；Amazon Research（亚马逊研究）；Department of Mechanical Engineering（机械工程系）；University of California at Berkeley（加州大学伯克利分校）；University of Amsterdam（阿姆斯特丹大学）；Courant Institute of Mathematical Sciences（柯朗数学科学研究所）；Hawai‘i Institute of Geophysics and Planetology（夏威夷地质与行星科学研究所）
NOWS: Neural Operator Warm Starts for Accelerating Iterative Solvers
Leibniz University Hannover（莱布尼茨汉诺威大学）；Institute of Photonics（光子研究所）；Department of Mathematics and Physics（数学与物理系）；Tsinghua University（清华大学）；Institute of Structural Mechanics（结构力学研究所）；Bauhaus-Universität Weimar（魏玛建筑学院）
Barcelona Neural Networking Center, Universitat Politècnica de Catalunya（巴塞罗那神经网络中心，加泰罗尼亚理工大学）；Huawei Technologies Co.,Ltd.（华为技术有限公司）
The recent growth of emergent network applications (e.g., satellite networks, vehicular networks) is increasing the complexity of managing modern communication networks. As a result, the community proposed the Digital Twin Networks (DTN) as a key enabler of efficient network management. Network operators can leverage the DTN to perform different optimization tasks (e.g., Traffic Engineering, Network Planning). Deep Reinforcement Learning (DRL) showed a high performance when applied to solve network optimization problems. In the context of DTN, DRL can be leveraged to solve optimization problem…
The University of Arizona（亚利桑那大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
GMRES is a powerful numerical solver used to find solutions to extremely large systems of linear equations. These systems of equations appear in many applications in science and engineering. Here we demonstrate a real-time machine learning algorithm that can be used to accelerate the time-to-solution for GMRES. Our framework is novel in that is integrates the deep learning algorithm in an in situ fashion: the AI-accelerator gradually learns how to optimizes the time to solution without requiring user input (such as a pre-trained data set). We describe how our algorithm collects data and optimi…
Indiana University Bloomington（印第安纳大学布卢明顿分校）；Indiana University - Purdue University Indianapolis（印第安纳大学-普渡大学印第安纳波利斯联合分校）
Nanopore genome sequencing is the key to enabling personalized medicine, global food security, and virus surveillance. The state-of-the-art base-callers adopt deep neural networks (DNNs) to translate electrical signals generated by nanopore sequencers to digital DNA symbols. A DNN-based base-caller consumes $44.5\%$ of total execution time of a nanopore sequencing pipeline. However, it is difficult to quantize a base-caller and build a power-efficient processing-in-memory (PIM) to run the quantized base-caller. In this paper, we propose a novel algorithm/architecture co-designed PIM, Helix, to…
Recent advances in deep learning have allowed neural networks (NNs) to successfully replace traditional numerical solvers in many applications, thus enabling impressive computing gains. One such application is time domain simulation, which is indispensable for the design, analysis and operation of many engineering systems. Simulating dynamical systems with implicit Newton-based solvers is a computationally heavy task, as it requires the solution of a parameterized system of differential and algebraic equations at each time step. A variety of NN-based methodologies have been shown to successful…
Shahid Beheshti University（沙希德·贝赫什提大学）；Max-Planck-Institut für Eisenforschung（马克斯·普朗克钢铁研究所）；RWTH Aachen University（亚琛工业大学）
Phase-field-based models have become common in material science, mechanics, physics, biology, chemistry, and engineering for the simulation of microstructure evolution. Yet, they suffer from the drawback of being computationally very costly when applied to large, complex systems. To reduce such computational costs, a Unet-based artificial neural network is developed as a surrogate model in the current work. Training input for this network is obtained from the results of the numerical solution of initial-boundary-value problems (IBVPs) based on the Fan-Chen model for grain microstructure evolut…
