Executive Summary : | Memory, in a crude sense, is the ability of any system to remember its past history, which can either be read out in later times or be erased. Now, studying memory formation in materials is of broad importance as it can be observed across many disciplines: physics, chemistry, biology and computer science. Memory is typically linked to the transient response of non-equilibrium systems as the system in equilibrium forgets its past evolutions. There can be many types of memory observed for diverse systems: glasses can remember their history of deformation, rubbers and rocks can retain the memory of the largest strain amplitude, magnets can have return point memory. Although memory formation is so common in non-equilibrium systems, there is no clear and coherent understanding of memory formation and its connection to the self-organization of system constituents. so, the proposed work will aim to unravel the physics behind memory formation and random organization in cyclically trained non-equilibrium systems such as granular materials and find efficient ways to train a disordered system to produce a specific mechanical response. The study will also shed light on the role of noise in formation of multiple transient memories in these types of systems. Large-scale numerical simulations and careful experiments will be performed to achieve the proposal's objectives. |