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DPOLY Short Course: The Gel, Elastomer, and Network Experience (GENE)
The DPOLY short course polymer networks, i.e. gels and elastomers, will provide students with a comprehensive introduction to the fundamental science of these ubiquitous materials as well as an introduction to emerging topics that will rapidly advance their knowledge towards the front of the field.
Saturday, March 3, 2018
1:00 p.m. - 5:00 p.m.
On the first day, the course will cover classic topics in gelation chemistry, structure, elasticity, and dynamic bonding, providing a fundamental introduction to network science at a level substantially deeper than a typical university physics course. The objective of this more fundamental part is to provide an overview of the field and the classical models used to define and describe networks.
Sunday, March 4, 2018
8:00 a.m. - 5:00 p.m.
The second day will be dedicated to select advanced topics inspired by recent research findings such as the relationship between network topology and elasticity, how to design molecular simulations of networks, self-assembled gels and liquid crystalline elastomers, and the methods of toughening elastomers and gels. The course will end with lectures from the rubber and biomedical industries, providing a perspective on application-driven needs in network science that will drive the field forward in years to come.
GSOFT Short Course: Machine Learning and Data Science in Soft Matter
Sunday, March 4
8:30 a.m. - 6:00 p.m.
Data-driven modeling approaches and machine learning have opened new paradigms in the understanding, engineering, and design of soft and biological materials. The advent of high-throughput experimental synthesis and characterization platforms, and the increasing prevalence of high-performance and multicore computer hardware have led to a deluge of data in soft matter. Analysis of these voluminous and multidimensional data sets requires soft matter researchers to implement and adapt tools from machine learning and data science. This one-day workshop will provide emerging and established soft matter researchers with exposure and training in machine learning and data science tools through a series of tutorials from some of the leading experts in the field. Topics to be covered include nonlinear manifold learning, enhanced sampling, materials informatics, and inverse soft materials design. Attendees will leave with both an appreciation for the state-of-the-art applications of data science in soft matter research, and a working knowledge of user-friendly Python libraries to implement these approaches in their own work.
Andrew Ferguson, University of Illinois at Urbana-Champaign
Eric Jankowski, Boise State University
Who Should Attend
This workshop is appropriate for all soft matter physicists who wish to integrate machine-learning tools into their domain-specific expertise. The course is expected to be particularly well-suited to those who have not received formal training in data science tools, but recognize the value of these approaches in advancing their own research endeavors. The workshop is designed to accommodate all levels of attendees from students and post-docs to established faculty members. Computational and experimental researchers are equally welcome.
The “Machine Learning with Python” session is a hands-on workshop. Attendees will bring their own laptop with a working Python installation equipped with the scikit-learn machine-learning library. Canopy and Anaconda provide free and easy-to-install Python releases equipped with scikit-learn and numerous other scientific libraries. Instructions are available online.
DBIO Short Course: Physics Meets Robotics: Hands-On Locomotion Robophysics
Sunday, March 4
9:00 a.m. - 5:00 p.m.
Robots are moving from the factory floor and into our lives (autonomous cars, homecare assistants, search and rescue devices, pets). However, despite the fascinating questions such future “living systems” pose for scientists, the study of such systems has been dominated by engineers and computer scientists. We propose that interaction of researchers studying dynamical systems, soft materials, and living systems can help discover principles which will allow physical robotic devices to interact with the real world in qualitatively different ways than they do now. This short course is designed to actively engage physicists in the challenges of robotics. Short course participants will design and construct small robots, studying how they can self-propel in different environments, and comparing the experiments to a geometric theory of self-propulsion. The short course will take place over a full day and enrollment will be limited to 30 participants; breakfast and snacks/coffee will be provided.
The course will begin the morning with two lectures (Goldman=experiments, Hatton=theory). Goldman will present an overview of locomotion robophysics and experimental techniques, and Hatton will discuss a framework for understanding robot (and animal) locomotion from a differential geometry perspective. Following the lectures, the participants will break into groups of 2-3 and each be given components with which each participant will construct his/her own small (15 cm long) “Purcell” robot (which he/she will get to keep). By the end of the day, under the guidance of Goldman and an assistant, each participant will have built and programmed robot composed of two servo motors, a microcontroller, LEGOs and 3D printed parts. Under the guidance of Hatton and an assistant, each participant will also gain familiarity with open-source software, which enables computation of geometric structures which facilitate prediction of optimal self-propulsion.
Daniel I. Goldman, Georgia Institute of Technology
Ross Hatton, Oregon State University
Who Should Attend
Students, postdocs, faculty and industrial researchers who are interested in getting some hands-on experience building robots and comparing to theory.