This thesis introduces the concept of "physics-based inverse design", working on the notion that the physical driving forces governing functionality are inherently encoded in independently... Show moreThis thesis introduces the concept of "physics-based inverse design", working on the notion that the physical driving forces governing functionality are inherently encoded in independently parameterized energy functions, which can be resolved through the use of inverse design strategies.The thesis describes the development of EVO-MD, a Python-based implementation of the physics-based inverse design concept. EVO-MD is capable of automatically setting-up, performing, and analyzing molecular dynamics simulations, allowing for the evolutionary optimization of complex and dynamic features in peptides. Examples of such applications include the optimization of lipid composition and curvature sensors, and the development of peptides with antiviral properties. Show less
On a structural level, the properties featured by a majority of mechanical metamaterials can be ascribed to the finite number of soft internal degrees-of freedom allowing for low-energy... Show moreOn a structural level, the properties featured by a majority of mechanical metamaterials can be ascribed to the finite number of soft internal degrees-of freedom allowing for low-energy deformations. Ideally, these low-energy deformation modes can be represented through mechanisms consisting of movable rigid geometrical units. Conversely, these mechanisms also serve as an intuitive starting point to initiate and adapt the design of mechanical metamaterials to requirements. Traditional design methods mainly comprising trial and testing can only well handle simple design tasks, not to mention that the final designs can be periodic and non-generic. In order to solve complex design problems, computer algorithm based inverse strategies provide state-of-the-art solutions. One way in which they can be utilized is by framing the material design problem as an optimization problem, where we optimize the values of control parameters (design variables) - in order to meet the desired target response. In this thesis, we present novel inverse strategies to design 2D mechanical metamaterials, whose zero-energy deformations can be modeled by one degree-of-freedom mechanisms consisting of pin-jointed polygons. We demonstrate that by optimizing the characteristic trajectory of these mechanisms, one can design generic metamaterials that exhibit complex programmable mechanics, atypical zero-energy deformations and shape-transformable behavior. Show less
The target of this work is to extend the canonical Evolution Strategies (ES) from traditional real-valued parameter optimization domain to mixed-integer parameter optimization domain. This is... Show moreThe target of this work is to extend the canonical Evolution Strategies (ES) from traditional real-valued parameter optimization domain to mixed-integer parameter optimization domain. This is necessary because there exist numerous practical optimization problems from industry in which the set of decision variables simultaneously involves continuous, integer and discrete variables. Furthermore, objective functions of this type of problems could be based on large-scale simulation models or the structure of the objective functions may be too complex to be modeled. From this perspective, optimization problems of this kind are classified into the black-box optimization category. For them, classic optimization techniques, which come from Mathematical Programming (MP) research field, cannot be easily applied, since they are based on the assumption that the search space can always be efficiently explored using a divide-and-conquer sche me. While our new proposed algorithm, the so-called Mixed-Integer Evolution Strategies (MIES), by contrast, is capable of yielding good solutions to these challenging black-box optimization problems by using specialized variation operators tailored for mixed-integer parameter classes. In this work not only did we study MIES intensively from a theoretical point of view, but also we develop the framework for applying MIES to the real-world optimization problem in the medical field. Show less
