Mathematical Modeling

Capturing Complexity

At Your Science | Mathematical Consulting, we provide mathematical modeling solutions, aimed at resolving real-world challenges to enhance strategic decision-making. Our expertise supports a diverse range of clients, including individual researchers, research teams, start-ups, medium-sized businesses, larger corporations, and state institutions.

We bridge your needs with our expertise throughout every phase of the project. Our support includes the followings steps:

Definition of the problem
Analysis of the real situation to be studied
Simplification of the real situation and creation of a real model
Translation of the real model into a mathematical model
Application of the appropriate toolboxes to the mathematical model and computation of a mathematical solution
Interpretation of the mathematical solution in the underlying real situation
Evaluation of the model’s performance: testing, sensitivity, robustness, and validation
Presentation of the results of the modeling process
Training encompassing model understanding, operation, and maintenance.

Our Focus

Deterministic Modeling: Utilizes provided inputs to yield a well-determined result, ensuring that identical inputs consistently produce the same output, without any randomness. Example: Calculating the trajectory of a spacecraft to Mars, where the initial position and speed determine the precise.

Stochastic Modeling: Incorporates randomness, allowing for variability in outcomes even with the same initial conditions. Example: Predicting weather patterns, utilizing probabilities to determine the most likely outcomes based on current data and model simulations.

Our Role

We leverage a systematic, analytic, and rigorous approach to tackle complex challenges, as for instance:

1. Understanding and Formulation: We identify essential features and the main structure of real-world problems, properly translating them into mathematical models.	2. Creativity and Innovation: Our solutions are marked by critical and in- dependent thinking, addressing, for instance, elements like uncertainty and local conditions that traditional algorithms may not capture.
3. Algorithm Design and Optimization: In partnership with programmers, we design algorithms for broader applicability and optimize them for enhanced efficiency, scalability, and robustness, by building on our deep mathematical understanding.	4. Communication and Collaboration: We are adept at scientific reporting and efficient teaching and training, facilitating clear communication across disciplines, translating complex concepts for a broad audience, and ensuring effective collaboration.