There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.

/pdf/using-deep-learning-for-image-based-plant-disease-detection-3ep7b222h5.pdf

Using Deep Learning for Image-Based Plant Disease Detection

Deep learning in agriculture: A survey

Study of the intricate connection between the design of material distributions (also called morphology or microstructure) and the final properties of the material system has been an attractive research theme for material science community. Such analysis provides ability to synthesize the microstructures exhibiting desired properties. This theme encompasses several material systems including porous materials [26], steels and welds [2], composites [14], powder metallurgy [28], 3D printing [22], energy storage devices as batteries [10], and energy converting devices like bulk hetero-junction solar cells [20]. Microstructure-sensitive design has been used to tailor a wide variety of properties including strengths, heat and mass diffusivities, energy storage capacity and lifetime, and energy conversion efficiency. Disciplines Computer-Aided Engineering and Design | Mechanical Engineering | Structural Materials Comments This is a manuscript of the article Shah, Viraj, Ameya Joshi, Balaji Sesha Sarath Pokuri, Sambuddha Ghosal, Soumik Sarkar, Baskar Ganapathysubramanian, and Chinmay Hegde. "Binary 2D Morphologies of Polymer Phase Separation: Dataset and Python Toolbox." (2019). Posted with permission. Authors Viraj Shah, Ameya Joshi, Balaji Sesha Sarath Pokuri, Sambuddha Ghosal, Soumik Sarkar, Baskar Ganapathysubramanian, and Chinmay Hegde This article is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/me_pubs/359 Binary 2D Morphologies of Polymer Phase Separation: Dataset and Python Toolbox Viraj Shah∗, Ameya Joshi, Balaji Sesha Sarath Pokuri, Sambuddha Ghosal, Soumik Sarkar, Baskar Ganapathysubramanian, Chinmay Hegde Iowa State University

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1361&context=me_pubs

Binary 2D Morphologies of Polymer Phase Separation: Dataset and Python Toolbox

Design of microparticles which stabilize at the centerline of a channel flow when part of a dilute suspension is examined numerically for moderate Reynolds numbers ($10 \le Re \le 80$). Stability metrics for particles with arbitrary shapes are formulated based on linear-stability theory. Particle shape is parametrized by a compact, Non-Uniform Rational B-Spline (NURBS)-based representation. Shape-design is posed as an optimization problem and solved using adaptive Bayesian optimization. We focus on designing particles for maximal stability at the channel-centerline robust to perturbations. Our results indicate that centerline-focusing particles are families of characteristic "fish"/"bottle"/"dumbbell"-like shapes, exhibiting fore-aft asymmetry. A parametric exploration is then performed to identify stable particle-designs at different k's (particle chord-to-channel width ratio) and Re's ($0.1 \le k \le 0.4, 10 \le Re \le 80$). Particles at high-k's and Re's are highly stabilized when compared to those at low-k's and Re's. A comparison of the modified dumbbell designs from the current framework also shows better performance to perturbations in Fluid-Structure Interaction (FSI) when compared to the rod-disk model reported previously (Uspal & Doyle 2014) for low-Re Hele-Shaw flow. We identify basins of attraction around the centerline, which span larger release-angle-ranges and lateral locations (tending to the channel width) for narrower channels, which effectively standardizes the notion of global focusing in such configurations using the current stability-paradigm. The present framework is illustrated for 2D cases and is potentially generalizable to stability in 3D flow-fields. The current formulation is also agnostic to Re and particle/channel geometry which indicates substantial potential for integration with imaging flow-cytometry tools and microfluidic biosensing-assays.

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1332&context=me_pubs

Shape-design for stabilizing micro-particles in inertial microfluidic flows

Many material systems of fundamental as well as industrial importance are significantly affected by underlying fluctuations and variations in boundary conditions, initial conditions, material property, as well as variabilities in operating and surrounding conditions. There has been increasing interest in analyzing, quantifying, and controlling the effects of such uncertain inputs on complex systems. This has resulted in the development of techniques in stochastic analysis and predictive modeling. A general application of stochastic techniques to many significant problems in engineering has been limited due to the lack of realistic, viable models of the input variability. Ideas of material informatics can be used to utilize available data about the input to construct a data-driven, computationally viable model of the input variability. This promising coupling of material informatics and stochastic analysis is investigated and some results are showcased using a simple example of analyzing diffusion in heterogeneous random media. Limited microstructural data is utilized to construct a realistic model of the thermal conductivity variability in a system. This reduced-order model then serves as the input to the stochastic partial differential equation describing thermal diffusion through random heterogeneous media.

Using data to account for lack of data: Linking material informatics with stochastic analysis

Surrogate modeling approach towards coupling computational fluid dynamics and energy simulations for analysis and design of energy efficient attics

In magnetic tape drives, lateral in-plane vibration of the tape leads to misalignment between data tracks and read/write head's position resulting in reduced storage capacity. To attenuate this lateral tape motion (LTM), surface guides-which include grooved, porous or roughened rollers-are used. The axial motion of the tape over the roller surface switches between two states: a sticking state when the axial force is smaller than the static frictional force; and a slipping state when the axial force is larger than the frictional force. A good understanding of the physical phenomena involved in this frictional interaction between the magnetic tape and surface friction guides will allow the appropriate and optimal choice of roller characteristics. We conduct a parametric study of frictional interaction between roller surface and the traveling magnetic tape by systematically varying the axial tension and transport velocity of the tape. An experimental setup is used to independently control these parameters and obtain lateral vibration measurements at two equidistant points-upstream and downstream-from the tape-roller interface. Techniques from spectral analysis are applied to the two signals to analyze and isolate the effect of stick-slip friction on LTM. It is noticed that the coherence function between the LTM signals provides valuable insight into the nature of stick-slip friction at the interface. We subsequently use it as a metric to construct and understand the “dynamic phase diagram,” i.e., to demarcate regions in the tension-velocity phase-space where predominance of stick or slip occurs.

Baskar Ganapathysubramanian

Papers

Binary 2D Morphologies of Polymer Phase Separation: Dataset and Python Toolbox

Shape-design for stabilizing micro-particles in inertial microfluidic flows

Using data to account for lack of data: Linking material informatics with stochastic analysis

Surrogate modeling approach towards coupling computational fluid dynamics and energy simulations for analysis and design of energy efficient attics

Exploring the Effect of Stick-Slip Friction Transition Across Tape-Roller Interface on the Transmission of Lateral Vibration