"Mathematical modeling of experimental results and prediction based on functions with a flexible structure" - book , written by Nikolai Kulikov , is a fascinating and in-depth study in the field of mathematical modeling, which opens new horizons for scientists , Students and Practitioners . This work will become an indispensable tool for those who seek to understand how mathematical methods can be applied to analyze and predict the results of experiments in various fields of science and technology. Kulikov masterfully combines theory and practice, offering readers not only theoretical foundations, but also practical examples that make the material accessible and understandable. The book covers a wide range of topics, including the construction of mathematical models, data analysis and forecasting methods, which makes it relevant for specialists in such fields as physics, biology, economics and engineering. Readers will be able to learn how to use functions with a flexible structure to create more accurate and adaptive models, which is especially important in a rapidly changing world. This book will be of interest to both students and experienced professionals. Students studying mathematics, statistics or related disciplines will find in it useful recommendations and practical tasks that will help to consolidate theoretical knowledge. Professionals working in research or industry will be able to apply the acquired knowledge to optimize their processes and improve the quality of decision-making. Kulikov offers readers not only theoretical calculations, but also practical tools that can be used in real projects. One of the key topics of the book is the flexibility of mathematical models. The author emphasizes that in conditions of uncertainty and variability of the environment, it is important to be able to adapt models to new data and conditions. This is especially true in the era of big data, when the amount of information available for analysis is growing rapidly. Kulikov shares his views on how to use modern computational methods for data processing and analysis, which makes his work especially valuable for specialists working with large amounts of information. Kulikov’s style is clear and accessible, which makes complex concepts understandable even for those who are just starting their way in mathematical modeling. It uses many examples and illustrations to help the reader better absorb the material. In addition, the author shares his experience and recommendations, which adds practical value to the book. Kulikov is also known for his other works on mathematics and its application in various fields. His research in statistics and probability theory has been recognized both among the academic community and among practitioners. Readers interested in deep and relevant topics, such as data analysis, forecasting, and mathematical modeling, will find much useful in his work. Mathematical modeling of experimental results and forecasting based on functions with a flexible structure is not just a textbook, but a whole world of opportunities for those who want to deepen their knowledge and skills in mathematical modeling. It will become a reliable companion for students, researchers and practitioners seeking new heights in their professional activities. If you are looking for a way to improve your skills in data analysis and forecasting, this book is your ideal choice.