Search Results for "mathematical modeling"
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Showing 52 open source projects for "mathematical modeling"
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MathModelAgent
An Agent Designed for Mathematical Modeling
MathModelAgent is an AI agent system designed specifically for assisting with mathematical modeling tasks and academic problem solving. The platform automates the process of analyzing mathematical problems, constructing models, generating code for simulations or computations, and producing a complete research-style report. The project uses a multi-agent architecture where different specialized agents handle tasks such as problem interpretation, modeling design, programming implementation, and paper writing. ... -
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MathModel
Mathematical Modeling for Graduate Students, Mathematical Modeling
...The repository is structured by topic and resource type so that users can more easily find templates, solved problems, and methodological notes. It also includes auxiliary educational materials like references, recommended textbooks, and guidebooks on mathematical modeling theory. -
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JuMP
Modeling language for Mathematical Optimization
JuMP is a modeling language and collection of supporting packages for mathematical optimization in Julia. JuMP makes it easy to formulate and solve a range of problem classes, including linear programs, integer programs, conic programs, semidefinite programs, and constrained nonlinear programs. JuMP is used to solve large-scale inventory routing problems at Renault, schedule trains at Thales Inc., plan power grid expansion at PSR, and route school buses. -
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FEATool Multiphysics
Physics Simulation Made Easy" (Fully Integrated FEA, FEniCS
FEATool Multiphysics (short for Finite Element Analysis Toolbox), is a fully integrated simulation platform for modeling all types of coupled physics phenomena, continuum mechanics, and engineering problems. By providing a truly easy to use and integrated all-in-one simulation platform for multi-physics analysis, makes FEATool an ideal simulation and modeling tool for everyone from students learning mathematical modeling, to researchers and engineers wishing to explore new ideas in a no fuss, simple, and convenient way. -
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MCM-ICM
Mathematical Contest resources
MCM-ICM is a curated archive of Outstanding Winner (“O-奖/特等奖”) solution papers from the Mathematical Contest in Modeling and the Interdisciplinary Contest in Modeling, spanning the early 2000s through recent years. The repository is organized by year, with per-year folders that collect the top-ranked reports and, in later years, additional materials such as problem statements or problem notes when available. It has evolved from a single-maintainer project into a collaborative effort, with different contributors stewarding successive year ranges so the collection stays current. ... -
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InfiniteOpt.jl
An intuitive modeling interface for infinite-dimensional optimization
A JuMP extension for expressing and solving infinite-dimensional optimization problems. InfiniteOpt.jl provides a general mathematical abstraction to express and solve infinite-dimensional optimization problems (i.e., problems with decision functions). Such problems stem from areas such as space-time programming and stochastic programming. InfiniteOpt is meant to facilitate intuitive model definition, automatic transcription into solvable models, permit a wide range of user-defined... -
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Surrogates.jl
Surrogate modeling and optimization for scientific machine learning
A surrogate model is an approximation method that mimics the behavior of a computationally expensive simulation. In more mathematical terms: suppose we are attempting to optimize a function f(p), but each calculation of f is very expensive. It may be the case we need to solve a PDE for each point or use advanced numerical linear algebra machinery, which is usually costly. The idea is then to develop a surrogate model g which approximates f by training on previous data collected from... -
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Yampa
Functional Reactive Programming domain-specific language
...It is grounded in strong mathematical foundations and is well-suited for real-time and interactive systems where temporal behaviors are central. -
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Book5_Essentials-Probability-Statistics
The book 5 of statistics in simplicity
...Its teaching philosophy prioritizes conceptual clarity over heavy formalism, making statistical thinking more approachable for beginners. The material connects probability theory directly to real analytical workflows, helping learners understand how statistics supports predictive modeling. Like the other books in the series, it blends mathematical explanation with Python-based experimentation. Overall, the project provides a practical statistical foundation for students advancing into AI and data science. -
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alphageometry
AI-driven neuro-symbolic solver for high-school geometry problems
AlphaGeometry, developed by Google DeepMind, is a theorem-proving system that combines symbolic reasoning with deep learning to solve challenging geometry problems, such as those found in mathematical Olympiads. The repository provides the full implementation of DDAR (Deductive Difference and Abductive Reasoning) and AlphaGeometry, two automated geometry solvers described in the 2024 Nature paper “Solving Olympiad Geometry without Human Demonstrations.” AlphaGeometry integrates a symbolic... -
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Finance
150+ quantitative finance Python programs
Finance is a repository that compiles structured notes and educational material related to financial analysis, markets, and quantitative finance concepts. The project focuses on explaining key principles used in finance and investment analysis, including topics such as financial statements, valuation models, portfolio theory, and financial markets. The repository is designed as a study reference for students and professionals who want to understand financial systems and the analytical... -
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Hasktorch
Tensors and neural networks in Haskell
Hasktorch is a powerful Haskell library for tensor computation and neural network modeling, built on top of libtorch (the backend of PyTorch). It brings differentiable programming, automatic differentiation, and efficient tensor operations into Haskell’s strongly typed functional paradigm. This project is in active development, so expect changes to the library API as it evolves. We would like to invite new users to join our Hasktorch discord space for questions and discussions.... -
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S2 Geometry
Computational geometry and spatial indexing on the sphere
...This makes it particularly suited for applications involving geospatial data, such as mapping, spatial indexing, and geographic information systems (GIS). The library provides a robust mathematical framework for spherical geometry, allowing developers to work with polygons, points, and regions on the Earth’s surface using consistent and precise algorithms. Unlike traditional 2D geometry libraries, S2 ensures accuracy over large scales by modeling the globe directly, avoiding distortions caused by map projections. The project includes core C++ support, optional Python bindings, and integrations with Bazel and CMake for flexible builds. ... -
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Computational Thinking
Introduction to computational thinking with Julia
Computational Thinking is an open source MIT course repository that teaches computational problem-solving through the Julia programming language. The course integrates mathematics, computing, and real-world applications into a unified curriculum, making it suitable for students across science, engineering, and data-driven fields. It emphasizes learning how to translate problems into computational terms and developing algorithms and models to analyze them effectively. Using Julia, the course... -
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PySINDy
A package for the sparse identification of nonlinear dynamical systems
PySINDy is a Python library that implements the Sparse Identification of Nonlinear Dynamics (SINDy) method for discovering mathematical models of dynamical systems from data. The framework focuses on identifying governing equations that describe the behavior of complex physical systems by selecting sparse combinations of candidate functions. Instead of fitting a purely predictive machine learning model, PySINDy attempts to recover interpretable differential equations that explain how a... -
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mum
Mathematical Approaches to User Modeling
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snorkel
A system for quickly generating training data with weak supervision
...The Snorkel project started at Stanford in 2016 with a simple technical bet: that it would increasingly be the training data, not the models, algorithms, or infrastructure, that decided whether a machine learning project succeeded or failed. Given this premise, we set out to explore the radical idea that you could bring mathematical and systems structure to the messy and often entirely manual process of training data creation and management, starting by empowering users to programmatically label, build, and manage training data. Snorkel Flow, an end-to-end machine learning platform for developing and deploying AI applications. Snorkel Flow incorporates many of the concepts of the Snorkel project with a range of newer techniques around weak supervision modeling, data augmentation, multi-task learning, data slicing and structuring. -
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Math Model
Code, resources, and templates for mathematical modeling
Math_Model is a repository collecting resources, code, and algorithm templates for mathematical modeling and competition (e.g. Chinese modeling contests, US undergraduate modeling competitions). It includes LaTeX templates for writing solutions, records of past contest problems and winning solutions, algorithm implementations in MATLAB / M scripts for optimization, intelligent algorithms, numerical methods, and model frameworks. -
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MathMod
MathMod is a mathematical modeling software
MathMod is a portable interactive plotting and graphing program for Windows, Linux, MacOSX and many other platforms. MathMod allows to plot 3D mathematical surfaces, described by implicit or parametric equations, and offers a very large database of model samples that can be generated with it. It's Free and Open Source. Change-log for MathMod-13.0 (15/11/2025) 1) Scripts generator to add thickness to iso/parametric surfaces 2) Undo/Redo commands for navigating through previous... -
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Optimizer_sovkov
Constructing and optimizing general mathematical and physical models
We present the package Optimizer, aimed at constructing and optimizing general mathematical models of phenomena of versatile nature. It is written in the Matlab algorithmic language and is executed in the Matlab environment with partial functionality in Octave. The convenient visual interface and the detailed manuals are provided. The main benefit of the package is its capability to construct models of any level of complexity in a block-by-block manner. Elementary model blocks can be... -
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Temporal Inference Engine
A real time inference engine for temporal logical specifications
...Specifications of signals and dynamic systems are represented as special graphs and executed in real time, with a predictable sampling time of few milliseconds. Real time signal processing, dynamic system control, state machine modeling and logical property verification are some fields of application of this software. The accepted language provides timed logic and mathematical operators, conditional operators, interval operators, bounded quantifiers and parametrization of signals. -
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PDP-OmniSim
PDP-OmniSim simulating parallel and distributed processing systems
PDP-OmniSim 🧬 Scientific Overview PDP-OmniSim is an advanced computational framework for simulating parallel and distributed processing systems, with cutting-edge applications in computational neuroscience, distributed computing, and complex systems modeling. The framework provides researchers with robust tools for large-scale simulations of networked systems and their emergent behaviors. 🎯 Key Scientific Contributions 🔬 Interdisciplinary Research Domains Computational... -
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picoGPT
An unnecessarily tiny implementation of GPT-2 in NumPy
picoGPT is a minimal implementation of the GPT-2 language model designed to demonstrate how transformer-based language models work at a conceptual level. The repository focuses on educational clarity rather than production performance, implementing the core components of the GPT architecture in a concise and readable way. It allows users to understand how tokenization, transformer layers, attention mechanisms, and autoregressive text generation operate in modern large language models. The... -
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Algorithms Math Models
MATLAB implementations of algorithms
Algorithms_MathModels is a large MATLAB collection of algorithms and solved examples targeted at students and teams preparing for mathematical modeling competitions (national and international contests like MCM/ICM). The repository gathers implementations and case studies across many topics commonly used in contest solutions: optimization (linear, integer, goal and nonlinear programming), heuristic and metaheuristic methods (simulated annealing, genetic algorithms, immune algorithms), neural networks and time-series methods, interpolation and regression, graph theory, cellular automata, grey systems, fuzzy models, partial/ordinary differential equations, and multivariate analysis, among others. ... -
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Pattern Recognition and Machine Learning
Repository of notes, code and notebooks in Python
Pattern Recognition and Machine Learning is an open-source repository that provides Python implementations and interactive notebooks for algorithms presented in the book Pattern Recognition and Machine Learning by Christopher Bishop. The project recreates many of the mathematical concepts and diagrams from the book using executable Jupyter notebooks, allowing readers to experiment directly with the algorithms described in the text. Each section of the repository corresponds to chapters in the book and includes code examples that demonstrate statistical modeling, machine learning methods, and Bayesian inference techniques. ...
