Solver
2015
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About 10,000 papers for “Solver”
Solver
John Pease, Moore Iv, H. N. Slater et al. · 2013
A high-order panel code capable of solving the potential flow equation about arbitrary curved geometries is presented. A new method for integrating curved, high-order panels using adaptive Gaussian quadrature is detailed. Furthermore, automated wake handling is addressed and a method to robustly solve for the steady-state free-wake rollup is proposed. Finally, a Fast Multipole Method with a complexity that scales as O(N) is also presented so that large problems can be handled using only a linear mesh. Results are presented to demonstrate high order accuracy and agreement with other inviscid solvers for a variety of test cases.
Cited by 0
Solver
Oleynikov, Andrew · Zenodo (CERN European Organization for Nuclear Research) · 2023
Cited by 0Open access
Solver
Landi, Germana · AMS Campus - AlmaDL · 2017
Cited by 0
Solver
Debra Lee Surface · Figshare · 2026
To give students hands-on experience with Multidimensional Scaling (MDS) perceptual mapping, the authors developed an assignment using a customized Excel spreadsheet that walks students through data collection and MDS analysis. Excel’s <i>Solver</i> tool performs the analysis, and perceptual maps are auto-generated. This approach circumvents using specialized software, leveraging students’ access to, and familiarity with, Excel – making it ideal for courses where the use of a statistical software package is not practical. The spreadsheet is available for download in Figshare and easily edited to accommodate any list of eight brands. Survey feedback is favorable for both graduate and undergraduate courses, and provides guidance for improving the experience for students.
Cited by 0Open access
Solver
Steven A. Wolfman · 2008
Recent advances in AI planning technology have drastically improved the capabilities of modern planners. However, these advances have left research in domain specialization behind; many older specialization techniques are no longer applicable to modern planners. This paper explores how to automate domain specialization in a modern planning as constraint satisfaction engine using existing analysis and reformulation techniques. In particular, the paper examines how TIM, PABLO, and Crawford et al.’s symmetry-breaking predicates aid in automating the CPlan planning as constraint satisfaction system. ∗ Thanks to Jeremy Buhler, Craig Kaplan, Rachel Pottinger, and Denise Pinnel for their help. Thanks also to my
Cited by 0
Solver
Steven A. Wolfman · 2008
Recent advances in AI planning technology have drastically improved the capabilities of modern planners. However, these advances have left research in domain specialization behind; many older specialization techniques are no longer applicable to modern planners. This paper explores how to automate domain specialization in a modern planning as constraint satisfaction engine using existing analysis and reformulation techniques. In particular, the paper examines how TIM, PABLO, and Crawford et al.’s symmetry-breaking predicates aid in automating the CPlan planning as constraint satisfaction system. ∗ Thanks to Jeremy Buhler, Craig Kaplan, Rachel Pottinger, and Denise Pinnel for their help. Thanks also to my
Cited by 0
Solver
Debra Lee Surface · Figshare · 2026
To give students hands-on experience with Multidimensional Scaling (MDS) perceptual mapping, the authors developed an assignment using a customized Excel spreadsheet that walks students through data collection and MDS analysis. Excel’s <i>Solver</i> tool performs the analysis, and perceptual maps are auto-generated. This approach circumvents using specialized software, leveraging students’ access to, and familiarity with, Excel – making it ideal for courses where the use of a statistical software package is not practical. The spreadsheet is available for download in Figshare and easily edited to accommodate any list of eight brands. Survey feedback is favorable for both graduate and undergraduate courses, and provides guidance for improving the experience for students.
Cited by 0Open access
Solver
Oleynikov, Andrew · Zenodo (CERN European Organization for Nuclear Research) · 2023
Cited by 0Open access
gulhameed361/TRF-Solver: TRF-Solver v0.0.0
Gul Hameed · Zenodo (CERN European Organization for Nuclear Research) · 2025
This is the original/first release (v0.0.0) of the surrogate-based grey-box optimisation solver linked to the manuscript: https://doi.org/10.48550/arXiv.2509.01651. Running- A sample grey box optimisation setup is included in the RunFile.py file between lines 269 and 311.- Additional problems (used in the manuscript: https://doi.org/10.48550/arXiv.2509.01651) are provided in the ProblemSet.py file and can be copied into the RunFile.py file.- Different solver options (such as surrogate forms, algorithmic variants introduced in the manuscript: https://doi.org/10.48550/arXiv.2509.01651, initial trust radius, maximum iterations, tolerances, etc.) can be set; a few are set as examples in the sample grey-box optimisation setup.- While optimising, the solver will display progress and results on Spyder's console via logger.- The iteration-wise solution (text file) will also be saved in the working directory. Benchmarking- Download the Benchmarking.zip file, and extract it to a directory.- There will be five ".py" files for five different black-box/derivative-free optimisation solvers in the extracted folder. Each ".py" file contains code for 25 problems.- Create a new environment using either: a. Benchmarking.yml (recommended for conda), or b. requirements.txt (for pip users in a virtualenv).- Activate the environment.- Open Spyder IDE, then open one of the ".py" files. For example, open Benchmarking-COBYLA.py and uncomment the formulation/code of problem 1 (i.e., P1). Run the code. Repeat the process for all 25 problems to reproduce benchmarking results for each DFO solver.
Cited by 0Open access
gulhameed361/TRF-Solver: TRF-Solver v0.0.1
Gul Hameed · Zenodo (CERN European Organization for Nuclear Research) · 2025
This release (v0.0.1) is linked to the manuscript: https://doi.org/10.48550/arXiv.2511.18998. A new globalisation strategy, 'Funnel', is added to the trust-region-based grey-box optimiser. Now, users can choose between filter and funnel globalisation strategies as solver options. Running Codes for all the grey-box optimisation problems presented in the manuscript (https://doi.org/10.48550/arXiv.2511.18998) are given between lines 325 and 2445 of RunFile.py After downloading all the source codes/files, uncomment the lines 325-398 in RunFile.py, and run this file to solve the Colville problem. The results will be displayed on the console via logger, and a solution text file will be generated. This step can be repeated for Himmelblau's Problem (lines 399-469), Loeppky function (472-530), Wing Weight function (533-614), Welded Beam problem (617-714), Williams-Otto process optimisation (716-841), and Biomass-based Hydrogen Production optimisation (844-2445). A user may optimise any grey-box problem by following the syntax used for the given problems. Different solver options (such as surrogate forms, globalisation strategies (filter or funnel) introduced in the manuscript: https://doi.org/10.48550/arXiv.2511.18998, algorithmic variants introduced in the manuscript: https://doi.org/10.48550/arXiv.2509.01651, initial trust radius, maximum iterations, tolerances, etc.) can be set; a few are set as examples in the provided grey-box optimisation setups.
Cited by 0Open access
gulhameed361/TRF-Solver: TRF-Solver v0.0.1
Gul Hameed · Zenodo (CERN European Organization for Nuclear Research) · 2025
This release (v0.0.1) is linked to the manuscript: https://doi.org/10.48550/arXiv.2511.18998. A new globalisation strategy, 'Funnel', is added to the trust-region-based grey-box optimiser. Now, users can choose between filter and funnel globalisation strategies as solver options. Running Codes for all the grey-box optimisation problems presented in the manuscript (https://doi.org/10.48550/arXiv.2511.18998) are given between lines 325 and 2445 of RunFile.py After downloading all the source codes/files, uncomment the lines 325-398 in RunFile.py, and run this file to solve the Colville problem. The results will be displayed on the console via logger, and a solution text file will be generated. This step can be repeated for Himmelblau's Problem (lines 399-469), Loeppky function (472-530), Wing Weight function (533-614), Welded Beam problem (617-714), Williams-Otto process optimisation (716-841), and Biomass-based Hydrogen Production optimisation (844-2445). A user may optimise any grey-box problem by following the syntax used for the given problems. Different solver options (such as surrogate forms, globalisation strategies (filter or funnel) introduced in the manuscript: https://doi.org/10.48550/arXiv.2511.18998, algorithmic variants introduced in the manuscript: https://doi.org/10.48550/arXiv.2509.01651, initial trust radius, maximum iterations, tolerances, etc.) can be set; a few are set as examples in the provided grey-box optimisation setups.
Cited by 0Open access
mohammadj93/hydraulic-network-solver: Hydraulic Network Solver
Mohammadj93 · Zenodo (CERN European Organization for Nuclear Research) · 2022
A MATLAB code to solve the flow, pressure and concentration distribution in a hydraulic network.
Cited by 1Open access
Laplace solver
Sanjay, K, Sinha, UN, Shankar, PN · Institutional Repository @ NAL (University of Southampton) · 1989
Laplace Equation is solved by schwarz' procedure in a domain composed of four sub domains wi th different flowsolver used in different sub domains
Cited by 0
Problem Solver
Cheryl Strauss Einhorn · Cornell University Press eBooks · 2023
Our decisions are an expression of who we are and how we move through the world. Rarely though, do we examine our decisions or even look inward to consider the psychology of our decision-making. Instead, we often make decisions based on what we call instinct—which relies on cognitive bias—false assumptions, mis-remembering and mental mistakes. Truthfully, we don't see the world as it is; we see it as we are. We can develop self-knowledge about our decision-making styles. We can wake ourselves up to how biases cloud our judgment and impede good decision-making—and we can counter bias. From there we can transform our decision-making habits to make better big decisions alone and together. Problem Solver provides you with tools to identify: • The five basic decision-making approaches, called 'Problem Solver Profiles': Adventurers, Detectives, Listeners, Thinkers and Visionaries • Your dominant—and secondary—Problem Solver Profile (PSP) • Tools to assess other peoples' PSPs • Each PSPs' decision-making strengths, blind spots, and biases • How your PSP impacts your outlook on life and risk appetite • How to use your PSP to maximize your decision strengths Replete with real-life examples and replicable strategies to put new decision-skills to your immediate benefit, Problem Solver will do more than help you look out into a future; it will equip you to move forward, with confidence, into your future.
Cited by 0
MoCap-solver
Kang Chen, Yupan Wang, Song–Hai Zhang et al. · ACM Transactions on Graphics · 2021
In a conventional optical motion capture (MoCap) workflow, two processes are needed to turn captured raw marker sequences into correct skeletal animation sequences. Firstly, various tracking errors present in the markers must be fixed ( cleaning or refining ). Secondly, an agent skeletal mesh must be prepared for the actor/actress, and used to determine skeleton information from the markers ( re-targeting or solving ). The whole process, normally referred to as solving MoCap data, is extremely time-consuming, labor-intensive, and usually the most costly part of animation production. Hence, there is a great demand for automated tools in industry. In this work, we present MoCap-Solver, a production-ready neural solver for optical MoCap data. It can directly produce skeleton sequences and clean marker sequences from raw MoCap markers, without any tedious manual operations. To achieve this goal, our key idea is to make use of neural encoders concerning three key intrinsic components: the template skeleton, marker configuration and motion, and to learn to predict these latent vectors from imperfect marker sequences containing noise and errors. By decoding these components from latent vectors, sequences of clean markers and skeletons can be directly recovered. Moreover, we also provide a novel normalization strategy based on learning a pose-dependent marker reliability function, which greatly improves system robustness. Experimental results demonstrate that our algorithm consistently outperforms the state-of-the-art on both synthetic and real-world datasets.
Cited by 23
fMPMM-solver
Wyser, Emmanuel, Alkhimenkov, Yury, Jayboyedoff, Michel et al. · Zenodo (CERN European Organization for Nuclear Research) · 2020
fMPMM-solver is an implementation of the Material Point Method and its variants (GIMP and CPDI2q) to solve 2D elasto-plastic problems in solid mechanics, such as granular collapse or slumping processes. fMPMM-solver is written in MATLAB language.
Cited by 0Open access
Griddlers Solver
Balcárek, Lukáš · Brno University of Technology Digital Library (Brno University of Technology) · 2010
Hlavním motivem této práce je logická hra Griddlers, na které jsou ukázány různé postupy pro hledání správného řešení. Pro nalezení řešení daného zadání se používají neinformované a informované algortimy, které jsou přizpůsobeny pro tuto logickou hru. Součástí této práce je vytvořená konzolová aplikace (demonstrující hledání výsledků ze zadaných metadat), na které je možné testovat různá zadání a pozorovat měnící se výpočetní nároky. Vytvořená aplikace má za cíl jednoduchým způsobem ukázat propojení umělé inteligence, algoritmizace a zpracování dat ve zvolené hře Griddlers.
Cited by 0
Sudoku Solver
Kai Preuà · OSF Preprints (OSF Preprints) · 2018
Cited by 0
Equation Solver
Unknown · OakTrust (Texas A&M University Libraries) · 2011
Cited by 0