What can you do when your system doesn't exist (yet)? You can create a model. It can be just a VHDL code, an equation set or a physical model described with a known theory. It's likely that you will need numerical analysis. We love numbers and mathematics and we can elaborate your model or invent a new one just for you. Modeling and numerical analysis can help you obtain more knowledge about the problem you are dealing. Xyste can increase your knowledge.

Mathematical analysis and modeling are important tools used in various fields such as physics, engineering, economics, and climate science. These tools help researchers to understand complex systems, make predictions, and optimize processes. In this essay, we present an overview of our mathematical analysis and modeling, including methods, and applications.

Mathematical analysis involves the study of mathematical functions and their properties using tools such as calculus, algebra, and differential equations. It allows researchers to analyze the behavior of systems and make predictions based on quantitative data. By studying the properties of functions, researchers can determine the rate of change, stability, and limits of a system. This information is essential for making informed decisions and optimizing processes in various fields.

Modeling, on the other hand, involves creating mathematical representations of real-world systems or phenomena. These models can be used to simulate the behavior of systems under different conditions and to identify optimal solutions. Models are based on assumptions and simplifications, so it is crucial to validate them using empirical data. Researchers use a combination of mathematical analysis and modeling to gain insights into complex systems that cannot be easily understood through observation alone. The images on this page show some real work Xyste has done in the field of maths and modeling, achieving high accurate predictions about the results of each project.

One common method used in mathematical modeling is creating differential equations to describe the relationship between various variables in a system. These equations can be solved analytically or numerically to predict the behavior of the system over time. For example, an electromagnetic sensor can be simulated starting from its differential equation, on one side, and using a finite element model on the other side. These two solutions can then be compared to verify each other, finally providing a great confidence about the design.

Another important aspect of mathematical modeling is parameter estimation, where researchers determine the values of unknown parameters in a model using data. This process involves fitting the model to experimental data through regression analysis or other statistical methods. Parameter estimation is crucial for validating models and ensuring that they accurately represent real-world systems. Such determination becomes relevant during electronic system design, using tools like SPICE.