Deep learning for the rare-event rational design of 3D printed multi-material mechanical metamaterials

Emerging multi-material 3D printing techniques have paved the way for the rational design of metamaterials with not only complex geometries but also arbitrary distributions of multiple materials within those geometries. Varying the spatial distribution of multiple materials gives rise to many interesting and potentially unique combinations of anisotropic elastic properties. While the availability of a design approach to cover a large portion of all possible combinations of elastic properties is interesting in itself, it is even more important to find the extremely rare designs that lead to highly unusual combinations of material properties (e.g., double-auxeticity and high elastic moduli). Here, we used a random distribution of a hard phase and a soft phase within a regular lattice to study the resulting anisotropic mechanical properties of the network in general and the abovementioned rare designs in particular. The primary challenge to take up concerns the huge number of design parameters and the extreme rarity of such designs. We, therefore, used computational models and deep learning algorithms to create a mapping from the space of design parameters to the space of mechanical properties, thereby (i) reducing the computational time required for evaluating each designand (ii) making the process of evaluating the different designs highly parallelizable. Furthermore, we selected ten designs to be fabricated using polyjet multi-material 3D printing techniques, mechanically tested them, and characterized their behavior using digital image correlation (DIC, 3 designs) to validate the accuracy of our computational models. The results of our simulations show that deep learning-based algorithms can accurately predict the mechanical properties of the different designs, which match the various deformation mechanisms observed in the experiments.

Application of Monte Carlo algorithms to cardiac imaging reconstruction

Monte Carlo algorithms have a growing impact on nuclear medicine reconstruction processes. One of the main limitations of myocardial perfusion imaging (MPI) is the effective mitigation of the scattering component, which is particularly challenging in Single Photon Emission Computed Tomography (SPECT). In SPECT, no timing information can be retrieved to locate the primary source photons. Monte Carlo methods allow an event-by-event simulation of the scattering kinematics, which can be incorporated into a model of the imaging system response. This approach was adopted since the late Nineties by several authors, and recently took advantage of the increased computational power made available by high-performance CPUs and GPUs. These recent developments enable a fast image reconstruction with an improved image quality, compared to deterministic approaches. Deterministic approaches are based on energy-windowing of the detector response, and on the cumulative estimate and subtraction of the scattering component. In this paper, we review the main strategies and algorithms to correct for the scattering effect in SPECT and focus on Monte Carlo developments, which nowadays allow the three-dimensional reconstruction of SPECT cardiac images in a few seconds.

A sequential resource investment planning framework using reinforcement learning and simulation-based optimization: A case study on microgrid storage expansion

A model and expansion plan have been developed to optimally determine microgrid designs as they evolve to dynamically react to changing conditions and to exploit energy storage capabilities. In the wake of the highly electrified future ahead of us, the role of energy storage is crucial wherever distributed generation is abundant, such as microgrid settings. Given the variety of storage options that are recently becoming more economical, determining which type of storage technology to invest in, along with the appropriate timing and capacity becomes a critical research question. In problems where the investment timing is of high priority, like this one, developing analytical and systematic frameworks for rigorously considering these issues is indispensable. From a business perspective, these strategic frameworks will aim to optimize the process of investment planning, by leveraging novel approaches and by capturing all the problem details that traditional approaches are unable to. Reinforcement learning algorithms have recently proven to be successful in problems where sequential decision-making is inherent. In the operations planning area, these algorithms are already used but mostly in short-term problems with well-defined constraints and low levels of uncertainty modeling. On the contrary, in this work, we expand and tailor these techniques to long-term investment planning by utilizing model-free approaches, like the Q-learning algorithm, combined with simulation-based models. We find that specific types of energy storage units, including the vanadium-redox battery, can be expected to be at the core of the future microgrid applications, and therefore, require further attention. Another key finding is that the optimal storage capacity threshold for a system depends heavily on the price movements of the available storage units in the market.