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Welcome to Forethought India One-on-One Courses. Our program is designed to provide students with the skills and knowledge necessary to excel in their chosen profession. Our instructor for this course is Researcher Mital Nitish at Imperial College London.


"Statistical Mechanics Essentials: Unraveling Complex Systems" is a comprehensive exploration of the fundamental principles and advanced applications of statistical mechanics. This course is designed to provide a deep understanding of how statistical mechanics plays a crucial role in unraveling complex systems found in various scientific domains. Starting with an introduction to the basic concepts and postulates, it delves into probability and statistics, ensembles, and partition functions. As the course progresses, it covers a wide range of topics, including quantum statistical mechanics, thermodynamics, phase transitions, and the statistical mechanics of diverse systems such as gases, solids, liquids, and polymers. It also delves into advanced areas like stochastic processes, machine learning, and the role of statistical mechanics in astrophysics and cosmology. Students will gain a solid foundation in statistical mechanics and explore its diverse applications in understanding the behavior of complex systems, making it a valuable resource for both beginners and those seeking to deepen their knowledge in this field.


At the end of the program, students will receive a certificate & performance letter by the eductor indicating that they have completed the program and acquired the necessary skills and knowledge to excel in their chosen profession.

Statistical Mechanics Essentials, by Mital Nitish at Imperial College London

  • Mital Nitish works on synthetic data generation using AI for AI, including the use of 3D rendering engines like Unity for environment modelling. He particularly focus on earth observation.
    He has done his Bachelor's and Masters in Electrical Engineering, Communications and Signal Processing from IIT-Bombay, 2010-15.
    He graduated in 2020 from Imperial College London working on erasure codes and error-correcting codes for distributed storage and distributed computing, secure multiparty computation, and wireless physical layer.
    He was a recipient of a fellowship from the H2020 Marie-Sklodowska Curie Scavenge training network.

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