Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/mbhuman/evgeny-and-models

The repository includes continuous modeling across various domains, with a special focus on biology and chemistry.
https://github.com/mbhuman/evgeny-and-models

Last synced: about 1 month ago
JSON representation

The repository includes continuous modeling across various domains, with a special focus on biology and chemistry.

Awesome Lists containing this project

README 

        
# evgeny-and-models

This repository offers a comprehensive collection of continuous modeling techniques across various scientific domains, with a strong focus on applications in biology and chemistry. The models are designed to tackle complex problems, including dynamic systems, population modeling, chemical reactions, and biological processes. Based on lectures from the Master's program at Moscow State University, the content combines theoretical insights with practical applications, providing valuable resources for students and researchers alike. It also includes detailed examples, code implementations, and case studies to facilitate learning and real-world problem-solving.



## Problem Structure

Each research or modeling problem in this repository is carefully designed to follow a structured format to ensure clarity and a deeper understanding of the problem and its solution. Every research must consist of the following sections:



### Context

Provides background information and sets the stage for the problem. This section explains the relevance of the problem within its domain (e.g., biology or chemistry), offering insights into the real-world scenario or scientific question being addressed.



### Problem

Clearly defines the core issue or challenge. This section identifies what needs to be solved, modeled, or optimized, presenting it in a precise and structured manner.



### Forces: 

Highlights the constraints, limitations, or conflicting factors that need to be considered. This may include trade-offs in the modeling process, assumptions, or other variables that influence the approach.



### Solution

Describes the modeling approach and the specific methods used to tackle the problem. This section includes continuous modeling techniques, algorithms, or mathematical frameworks applied to solve the problem.



### Result Context

Summarizes the outcomes, interpretations, and implications of the solution. It explains how the results relate to the initial context and the broader domain, evaluating the effectiveness and impact of the solution.



### Related Problems

Lists similar or related problems that share common elements, offering opportunities for further exploration or cross-domain learning.



### Learn More

Provides references, additional reading materials, and resources that can help the reader dive deeper into the subject, including related research papers, textbooks, or tutorials.



## Additional Valuable Features



### Practical Case Studies

Each problem includes real-world examples, ensuring the practical applicability of the models. These case studies help bridge the gap between theory and practice.



### Code Implementation

Wherever applicable, code implementations are provided, allowing users to experiment with and adapt models to their specific research needs.



### Cross-Domain Insights

The repository promotes interdisciplinary learning by showing how techniques from one domain, such as biology, can be applied in others, like chemistry, enabling users to expand their problem-solving toolkit.



## Conclusion

This structured approach ensures that each research problem is comprehensive, clear, and educational, making it easier for users to understand the nuances of continuous modeling across various domains.