Global Emulation Through Normative Decision Making and Thrifty Adaptive Batch Sampling

2019 ◽  
Author(s):  
Anton van Beek ◽  
Siyu Tao ◽  
Wei Chen
Keyword(s):  
Decision Making ◽  
Adaptive Sampling ◽  
Computational Cost ◽  
Sampling Strategy ◽  
Batch Size ◽  
Computational Time ◽  
Sampling Techniques ◽  
Reward Function ◽  
Minimal Reduction ◽  
Numerical Complexity

Abstract We consider the problem of adaptive sampling for global emulation (metamodeling) with a finite budget. Conventionally this problem is tackled through a greedy sampling strategy, which is optimal for taking either a single sample or a handful of samples at a single sampling stage but neglects the influence of future samples. This raises the question: “Can we optimize the number of sampling stages as well as the number of samples at each stage?” The proposed thrifty adaptive batch sampling (TABS) approach addresses this challenge by adopting a normative decision-making perspective to determine the total number of required samples and maximize a multistage reward function with respect to the total number of stages and the batch size at each stage. To amend TABS’ numerical complexity we propose two heuristic-based strategies that significantly reduce computational time with minimal reduction of reward optimality. Through numerical examples, TABS is shown to outperform or at least be comparable to conventional greedy sampling techniques. In this fashion, TABS provides modelers a flexible adaptive sampling tool for global emulation, effectively reducing computational cost while maintaining prediction accuracy.

Integration of Normative Decision-Making and Batch Sampling for Global Metamodeling

2020 ◽  
Vol 142 (3) ◽  
Author(s):  
Anton van Beek ◽  
Siyu Tao ◽  
Matthew Plumlee ◽  
Daniel W. Apley ◽  
Wei Chen
Keyword(s):  
Adaptive Sampling ◽  
Sampling Strategy ◽  
Risk Attitude ◽  
Spatial Location ◽  
Reward Function ◽  
Minimal Reduction ◽  
The Cost ◽  
Global Metamodeling ◽  
Costly Function ◽  
Numerical Complexity

Abstract The cost of adaptive sampling for global metamodeling depends on the total number of costly function evaluations and to which degree these evaluations are performed in parallel. Conventionally, samples are taken through a greedy sampling strategy that is optimal for either a single sample or a handful of samples. The limitation of such an approach is that they compromise optimality when more samples are taken. In this paper, we propose a thrifty adaptive batch sampling (TABS) approach that maximizes a multistage reward function to find an optimal sampling policy containing the total number of sampling stages, the number of samples per stage, and the spatial location of each sample. Consequently, the first batch identified by TABS is optimal with respect to all potential future samples, the available resources, and is consistent with a modeler’s preference and risk attitude. Moreover, we propose two heuristic-based strategies that reduce numerical complexity with a minimal reduction in optimality. Through numerical examples, we show that TABS outperforms or is comparable with greedy sampling strategies. In short, TABS provides modelers with a flexible adaptive sampling tool for global metamodeling that effectively reduces sampling costs while maintaining prediction accuracy.

Robust Well Placement Optimization Through Universal Trace Kriging with Adaptive Sampling

2021 ◽  
Author(s):  
Carlo Cristiano Stabile ◽  
Marco Barbiero ◽  
Giorgio Fighera ◽  
Laura Dovera
Keyword(s):  
Adaptive Sampling ◽  
Pareto Front ◽  
Computing Time ◽  
Computational Cost ◽  
Oil Field ◽  
Computational Time ◽  
Decision Space ◽  
Adaptive Approach ◽  
Reservoir Simulations ◽  
Proxy Models

Abstract Optimizing well locations for a green field is critical to mitigate development risks. Performing such workflows with reservoir simulations is very challenging due to the huge computational cost. Proxy models can instead provide accurate estimates at a fraction of the computing time. This study presents an application of new generation functional proxies to optimize the well locations in a real oil field with respect to the actualized oil production on all the different geological realizations. Proxies are built with the Universal Trace Kriging and are functional in time allowing to actualize oil flows over the asset lifetime. Proxies are trained on the reservoir simulations using randomly sampled well locations. Two proxies are created for a pessimistic model (P10) and a mid-case model (P50) to capture the geological uncertainties. The optimization step uses the Non-dominated Sorting Genetic Algorithm, with discounted oil productions of the two proxies, as objective functions. An adaptive approach was employed: optimized points found from a first optimization were used to re-train the proxy models and a second run of optimization was performed. The methodology was applied on a real oil reservoir to optimize the location of four vertical production wells and compared against reference locations. 111 geological realizations were available, in which one relevant uncertainty is the presence of possible compartments. The decision space represented by the horizontal translation vectors for each well was sampled using Plackett-Burman and Latin-Hypercube designs. A first application produced a proxy with poor predictive quality. Redrawing the areas to avoid overlaps and to confine the decision space of each well in one compartment, improved the quality. This suggests that the proxy predictive ability deteriorates in presence of highly non-linear responses caused by sealing faults or by well interchanging positions. We then followed a 2-step adaptive approach: a first optimization was performed and the resulting Pareto front was validated with reservoir simulations; to further improve the proxy quality in this region of the decision space, the validated Pareto front points were added to the initial dataset to retrain the proxy and consequently rerun the optimization. The final well locations were validated on all 111 realizations with reservoir simulations and resulted in an overall increase of the discounted production of about 5% compared to the reference development strategy. The adaptive approach, combined with functional proxy, proved to be successful in improving the workflow by purposefully increasing the training set samples with data points able to enhance the optimization step effectiveness. Each optimization run performed relies on about 1 million proxy evaluations which required negligible computational time. The same workflow carried out with standard reservoir simulations would have been practically unfeasible.

A Novel Adaptive Sampling Strategy for Deep Reinforcement Learning

2021 ◽  
Vol 20 (02) ◽  
pp. 2150011
Author(s):  
Xingxing Liang ◽  
Li Chen ◽  
Yanghe Feng ◽  
Zhong Liu ◽  
Yang Ma ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Adaptive Sampling ◽  
Learning Algorithm ◽  
Sampling Strategy ◽  
Sequential Decision ◽  
Fixed Temperature ◽  
Sample Distribution ◽  
Intelligent Decision Making ◽  
Experience Replay

Reinforcement learning, as an effective method to solve complex sequential decision-making problems, plays an important role in areas such as intelligent decision-making and behavioral cognition. It is well known that the sample experience replay mechanism contributes to the development of current deep reinforcement learning by reusing past samples to improve the efficiency of samples. However, the existing priority experience replay mechanism changes the sample distribution in the sample set due to the higher sampling frequency assigned to a specific transition, and it cannot be applied to actor-critic and other on-policy reinforcement learning algorithm. To address this, we propose an adaptive factor based on TD-error, which further increases sample utilization by giving more attention weight to samples of larger TD-error, and embeds it flexibly into the original Deep Q Network and Advantage Actor-Critic algorithm to improve their performance. Then we carried out the performance evaluation for the proposed architecture in the context of CartPole-V1 and 6 environments of Atari game experiments, respectively, and the obtained results either on the conditions of fixed temperature or annealing temperature, when compared to those produced by the vanilla DQN and original A2C, highlight the advantages in cumulative rewards and climb speed of the improved algorithms.

Reliability Analysis Method Based on Support Vector Machines Classification and Adaptive Sampling Strategy

2012 ◽  
Vol 544 ◽  
pp. 212-217 ◽  
Author(s):  
Hong Yan Hao ◽  
Hao Bo Qiu ◽  
Zhen Zhong Chen ◽  
Hua Di Xiong
Keyword(s):  
Reliability Analysis ◽  
Adaptive Sampling ◽  
Limit State ◽  
Computational Cost ◽  
Sampling Strategy ◽  
Support Vector ◽  
State Function ◽  
Analysis Method ◽  
Design Problems ◽  
Vector Machines

For probabilistic design problems with implicit limit state functions encountered in practical application, it is difficult to perform reliability analysis due to the expensive computational cost. In this paper, a new reliability analysis method which applies support vector machine classification(SVM-C) and adaptive sampling strategy is proposed to improve the efficiency. The SVM-C constructs a model defining the boundary of failure regions which classifies samples as safe or failed using SVM-C, then this model is used to replace the true limit state function,thus reducing the computational cost. The adaptive sampling strategy is applied to select samples along the constraint boundaries. It can also improves the efficiency of the proposed method. In the end, a probability analysis example is presented to prove the feasible and efficient of the proposed method.

Advanced time-dependent reliability analysis based on adaptive sampling region with Kriging model

2020 ◽  
Vol 234 (4) ◽  
pp. 588-600
Author(s):  
Yan Shi ◽  
Zhenzhou Lu ◽  
Ruyang He
Keyword(s):  
Reliability Analysis ◽  
Failure Probability ◽  
Surrogate Model ◽  
Adaptive Sampling ◽  
Computational Cost ◽  
Kriging Model ◽  
Time Dependent ◽  
Computational Time ◽  
Optimization Strategy ◽  
Dependent Failure

Aiming at accurately and efficiently estimating the time-dependent failure probability, a novel time-dependent reliability analysis method based on active learning Kriging model is proposed. Although active surrogate model methods have been used to estimate the time-dependent failure probability, efficiently estimating the time-dependent failure probability by a fewer computational time remains an issue because screening all the candidate samples iteratively by the active surrogate model is time-consuming. This article is intended to address this issue by establishing an optimization strategy to search the new training samples for updating the surrogate model. The optimization strategy is performed in the adaptive sampling region which is first proposed. The adaptive sampling region is adjustable by the current surrogate model in order to provide a proper candidate samples region of the input variables. The proposed method employs the optimization strategy to select the optimal sample to be the new training sample point in each iteration, and it does not need to predict the values of all the candidate samples at every time instant in each iterative step. Several examples are introduced to illustrate the accuracy and efficiency of the proposed method for estimating the time-dependent failure probability by simultaneously considering the computational cost and precision.

scholarly journals Adaptive infill sampling strategy for metamodeling: Challenge and future research directions

2020 ◽  
Vol 9 (5) ◽  
pp. 2020-2029
Author(s):  
Che Munira Che Razali ◽  
Shahrum Shah Abdullah ◽  
Amir Parnianifard ◽  
Amrul Faruq
Keyword(s):  
Simulation Model ◽  
Adaptive Sampling ◽  
Computational Cost ◽  
Computer Experiments ◽  
Sampling Strategy ◽  
Future Research ◽  
Expensive Function ◽  
Modern Engineering ◽  
Homogeneous Method ◽  
Improving Accuracy

The widespread use of computer experiments for design optimization has made the issue of reducing computational cost, improving accuracy, removing the “curse of dimensionality” and avoiding expensive function approximation becoming even more important. Metamodeling also known as surrogate modeling, can approximate the actual simulation model allowing for much faster execution time thus becoming a useful method to mitigate these problems. There are two (2) well-known metamodeling techniques which is kriging and radial basis function (RBF) discussed in this paper based on widely used algorithm tool from previous work in modern engineering design of optimization. An integral part of metamodeling is in the method to sample new data from the actual simulation model. Sampling new data for metamodeling requires finding the location (or value) of one or more new data such that the accuracy of the metamodel can be increased as much as possible after the sampling process. This paper discussed the challenges of adaptive sampling in metamodel and proposed an ensemble non-homogeneous method for best model voting to obtain new sample points.

Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

2020 ◽  
Author(s):  
Jingbai Li ◽  
Patrick Reiser ◽  
André Eberhard ◽  
Pascal Friederich ◽  
Steven Lopez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Excited State ◽  
Adaptive Sampling ◽  
Computational Cost ◽  
Ground Truth ◽  
Absolute Error ◽  
Photochemical Reactions ◽  
Computational Techniques ◽  
Full Potential

<p>Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr <i>et al.</i> demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH<sub>2</sub>NH<sub>2</sub><sup>+</sup>).</p><p>We have developed a Python-based code, Python Rapid Artificial Intelligence <i>Ab Initio</i> Molecular Dynamics (PyRAI<sup>2</sup>MD), to accomplish the unprecedented 10 ns <i>cis-trans</i> photodynamics of <i>trans</i>-hexafluoro-2-butene (CF<sub>3</sub>–CH=CH–CF<sub>3</sub>) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S<sub>1</sub> half-life (60.5 fs), the photochemical product ratio (<i>trans</i>: <i>cis</i> = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (<i>trans</i> → <i>cis</i> but not <i>cis</i> → <i>trans</i> pathways) that may offer future automated photochemical reaction discoveries.</p>

Improving the Computational Cost for Copied Region Detection in Forensic Images

2016 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Tu Huynh-Kha ◽  
Thuong Le-Tien ◽  
Synh Ha ◽  
Khoa Huynh-Van
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Research Work ◽  
Computational Cost ◽  
Correlation Coefficients ◽  
Zernike Moments ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

PENGARUH KUALITAS PRODUK DAN PROMOSI TERHADAP KEPUTUSAN PEMBELIAN BUAH MELON PT. SYAFINA NIAGA

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Iwan Kurniawan Subagja, SE., MM. ◽  
Slamet Istoto
Keyword(s):  
Decision Making ◽  
Product Quality ◽  
Sampling Methods ◽  
Marketing Mix ◽  
Sampling Techniques ◽  
Purchasing Decisions ◽  
Individual Activity ◽  
A Company ◽  
Melon Fruit

Decision making is an individual activity that is directly involved in obtaining and using the goods offered. Promotion is one of the variables in a marketing mix that is very important to be implemented by a company in marketing its products or services, thus influencing consumers to make purchasing decisions. The quality of the product can also affect consumers in making purchasing decisions. This study aims to analyze the effect of product quality and promotion on purchasing decision of Melon fruit. Population and sample of research is purchasing or buyer from consumer of PT. Syafina Niaga as many as 30 companies and sampling methods using saturated sampling techniques as well as analysis used with regression analysis. The results showed that there is a significant influence between product quality and promotion of purchasing decisions.

scholarly journals A divide-and-conquer algorithm for quantum state preparation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Israel F. Araujo ◽  
Daniel K. Park ◽  
Francesco Petruccione ◽  
Adenilton J. da Silva
Keyword(s):  
Computational Cost ◽  
Quantum Circuit ◽  
Divide And Conquer ◽  
Computational Time ◽  
Quantum Computers ◽  
Dimensional Vector ◽  
Quantum Devices ◽  
Divide And Conquer Algorithm ◽  
Quantum State Preparation ◽  
Quantum Device

AbstractAdvantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices.

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