28 papers · ranked by Valyu relevance
Hissah Albaqami, Mehdi Mrad, Anis Gharbi, Munevver Mine Subasi + 1 more
This paper presents a Monte Carlo simulation-based approach for solving stochastic two-stage bond portfolio optimization problems. The main objective is to optimize the cost of the bond portfolio while making decisions on bond purchases, holdings, and sales under random market conditions such as interest rate…
Xiaohong Chen, Zengjing Chen, Xiaodong Yan, Guodong Zhang + 1 more
For a general purpose optimization problem over a finite rectangle region, this paper pioneers a unified slot machine framework for global optimization by transforming the search for global optimizer(s) to the optimal strategy formulation of a bandit process in infinite policy sets and proves that two-armed bandit is…
Mikhail Finko, Batikan Koroglu, Kate E. Rodriguez, Timothy P. Rose + 4 more
'Jonathan C. Crowhurst' 'Davide Curreli' 'Harry B. Radousky' 'Kim B. Knight'] In this work, a coupled Monte Carlo Genetic Algorithm (MCGA) approach is used to optimize a gas phase uranium oxide reaction mechanism based on plasma flow reactor (PFR) measurements. The PFR produces a steady Ar plasma containing U, O, H…
Ingrid Reiweger, Manuel Genswein, Peter Paal, Jürg Schweizer + 1 more
'Chiara Lazzeri'] Recent technical and strategical developments have increased the survival chances for avalanche victims. Still hundreds of people, primarily recreationists, get caught and buried by snow avalanches every year. About 100 die each year in the European Alps-and many more worldwide. Refining concepts for…
Jiří Mazurek, Dominik Strzałka, Gabriele Oliva
In multiple-criteria decision making/aiding/analysis (MCDM/MCDA) weights of criteria constitute a crucial input for finding an optimal solution (alternative). A large number of methods were proposed for criteria weights derivation including direct ranking, point allocation, pairwise comparisons, entropy method…
Callum M. Macdonald, Simon Arridge, Samuel Powell
Significance: Indirect imaging problems in biomedical optics generally require repeated evaluation of forward models of radiative transport, for which Monte Carlo is accurate yet computationally costly. We develop an approach to reduce this bottleneck, which has significant implications for quantitative tomographic…
Authors not listed
Continuous manufacturing processes offer significant advantages over batch processes, including easier scalability, reduced costs, lower raw material and solvent consumption, and improved energy efficiency. A robust techno-economic assessment is therefore essential to evaluate and facilitate the adoption of such…
Samuel Gill, Nathan M. Lim, Patrick Grinaway, Ariën S. Rustenburg + 4 more
Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement…
Satyajith Amaran, Nikolaos V. Sahinidis, Bikram Sharda, Scott J. Bury
'Scott J. Bury'] Simulation Optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation—discrete or continuous decisions, expensive or cheap simulations, single or…
Ronnie Henry
A Markov chain Monte Carlo (MCMC) simulation is a method of estimating an unknown probability distribution for the outcome of a complex process (a posterior distribution). Prior (capturing the concept prior to seeing any data) distributions are used to simulate sampling from variables that have known or closely…
Ayush Garg, Shyam Sundar Das, Naveen Sivadasan, Arijit Roy + 1 more
Optimizing dose and schedule remains a central challenge in oncology drug development, particularly for immunotherapies where fixed dosing regimens often fail to account for patient specific heterogeneity in tumor–immune dynamics. Here, we present a hybrid quantitative systems pharmacology–reinforcement learning–Monte…
Samuel Gill, Nathan M. Lim, Patrick Grinaway, Ariën S. Rustenburg + 4 more
Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement…
Samuel Gill, Nathan M. Lim, Patrick Grinaway, Ariën S. Rustenburg + 4 more
Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement…
Chris Rackauckas, Vaibhav Dixit, Adam R. Gerlach, Vijay Ivaturi
Personalized precision dosing is about mathematically determining effective dosing strategies that optimize the probability of containing a patient’s outcome within a therapeutic window. However, the common Monte Carlo approach for generating patient statistics is computationally expensive because thousands of…
Dantong Wang, Paul Stapor, Jan Hasenauer
Mixed effect modeling is widely used to study cell-to-cell and patient-to-patient variability. The population statistics of mixed effect models is usually approximated using Dirac mixture distributions obtained using Monte-Carlo, quasi Monte-Carlo, and sigma point methods. Here, we propose the use of a method based on…
Jérémi Dauchet, Jean-Jacques Bezian, Stéphane Blanco, Cyril Caliot + 15 more
'Julien Charon' 'Christophe Coustet' 'Mouna El Hafi' 'Vincent Eymet' 'Olivier Farges' 'Vincent Forest' 'Richard Fournier' 'Mathieu Galtier' 'Jacques Gautrais' 'Anaïs Khuong' 'Lionel Pelissier' 'Benjamin Piaud' 'Maxime Roger' 'Guillaume Terrée' 'Sebastian Weitz'] Monte Carlo is famous for accepting model extensions and…
Justin L. MacCallum, Mir Ishruna Muniyat, Kari Gaalswyk
Replica exchange is a widely used sampling strategy in molecular simulation. While a variety of methods exist for optimizing temperature replica exchange, less is known about how to optimize more general Hamiltonian replica exchange simulations. We present an algorithm for the on-line optimization of both temperature…
Joseph W Abbott, Felix Hanke
We present a kinetic approach to the Monte Carlo-molecular dynamics (MC-MD) method for simulating reactive liquids using non-reactive forcefields. A graphical reaction representation allows definition of reactions of arbitrary complexity, including their local solvation environment. Reaction probabilities and molecular…
Jiyizhe Zhang, Daria Semochkina, Naoto Sugisawa, David Woods + 1 more
Multi-objective Bayesian optimization (MOBO) has shown to be a promising tool for reaction development. However, noise is usually unavoidable during experiments and makes it challenging to find reliable solutions. In this study, we focus on finding a set of optimal reaction conditions using multi-objective Euclidian…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Abhilash Nandy, Chandan Kumar‐Sinha, Deepak Mewada, Soumya Sharma
In this report we survey Bayesian Optimization methods focussed on Multi-Armed Bandit Problem. We take the help of the paper "Portfolio Allocation for Bayesian Optimization" [1]. We report a small literature survey on the acquisition functions and the types of portfolio strategies used in the papers [1] [2]. We also…
Cheng Li, Santu Rana, Sunil Gupta, Vu Nguyen + 7 more
'Alessandra Sutti' 'David Rubin' 'Teo Slezak' 'Murray Height' 'Mazher Iqbal Mohammed' 'Ian Gibson'] Abstract—Experimental design is a process of obtaining a product with target property via experimentation. Bayesian optimization offers a sample-efficient tool for experimental design when experiments are expensive.…
Pushparaja Murugan
Convolutional Neural Network is known as ConvNet have been extensively used in many complex machine learning tasks. However, hyperparameters optimization is one of a crucial step in developing ConvNet architectures, since the accuracy and performance are totally reliant on the hyperparameters. This multilayered…
Ran Rubin
Bayesian optimization has emerged as a strong candidate tool for global optimization of functions with expensive evaluation costs. However, due to the dynamic nature of research in Bayesian approaches, and the evolution of computing technology, using Bayesian optimization in a parallel computing environment remains a…
Ashish Anil Pawar, Ujwal Warbhe
Bayesian Optimization is an effective method for searching the global maxima of an objective function especially if the function is unknown. The process comprises of using a surrogate function and choosing an acquisition function followed by optimizing the acquisition function to find the next sampling point. This…
Hua-Dong Xiong, Li Ji-An, Marcelo G. Mattar, Robert C. Wilson
Cognitive modeling provides a formal method to articulate and test hypotheses about cognitive processes. However, accurately and reliably estimating model parameters remains challenging due to common issues in behavioral science, such as limited data, measurement noise, experimental constraints, and model complexity.…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…