Solving differential equations is a critical challenge across a host of domains. While many software packages efficiently solve these equations using classical numerical approaches, there has been less effort in developing a library for researchers interested in solving such systems using neural networks. With PyTorch as its backend, NeuroDiffEq is a software library that exploits neural networks to solve differential equations. In this paper, we highlight the latest features of the NeuroDiffEq library since its debut. We show that NeuroDiffEq can solve complex boundary value problems in arbitrary dimensions, tackle boundary conditions at infinity, and maintain flexibility for dynamic injection at runtime.

Predicting how climate and the environment will change over time or how air flows over an aircraft are problems too complex even for the most powerful supercomputers to solve. Scientists rely on models to fill in the gap between what they can simulate and what they need to predict. But, as every meteorologist knows, models often rely on partial or even faulty information which may lead to bad predictions. Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) are forming what they call "intelligent alloys", combining the power of computational science with artificial intelligence to develop models that complement simulations to predict the evolution of science's most complex systems. In a paper published in Nature Communications, Petros Koumoutsakos, the Herbert S. Winokur, Jr. Professor of Engineering and Applied Sciences and co-author Jane Bae, a former postdoctoral fellow at the Institute of Applied Computational Science at SEAS, combined reinforcement learning with numerical methods to compute turbulent flows, one of the most complex processes in engineering.

Soft robots driven by pressurized fluids could explore new frontiers and interact with delicate objects in ways that traditional rigid robots can't. But building entirely soft robots remains a challenge because many of the components required to power these devices are, themselves, rigid. Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed electrically-driven soft valves to control hydraulic soft actuators. These valves could be used in assistive and therapeutic devices, bio-inspired soft robots, soft grippers, surgical robots, and more. The research was published in the Proceedings of the National Academy of Sciences (PNAS).

Robotic grasping and manipulation has historically been dominated by rigid grippers, force/form closure constraints, and extensive grasp trajectory planning. The advent of soft robotics offers new avenues to diverge from this paradigm by using strategic compliance to passively conform to grasped objects in the absence of active control, and with minimal chance of damage to the object or surrounding environment. However, while the reduced emphasis on sensing, planning, and control complexity simplifies grasping and manipulation tasks, precision and dexterity are often lost. This talk will discuss efforts to increase the robustness of soft grasping and the dexterity of soft robotic manipulators, with particular emphasis on grasping tasks that are challenging for more traditional robot hands. This includes compliant objects, thin flexible sheets, and delicate organisms.

There are a variety of traits in humans that make them human. While science is progressing and technology is being developed to capture and adapt human traits, we are yet to figure out various mysteries of the human brain. Predicting someone's behaviour based on their actions is perhaps one of the most human things that can be witnessed. In a recent study, researchers from the Columbia University School of Engineering and Applied Science may have unveiled AI technology that can help predict human behaviour. The study is called "Learning the Predictability of the Future" and was presented at the Conference on Computer Vision and Pattern Recognition that took place from 15 - June to 25 June 2021.

Minimally invasive surgeries in which surgeons gain access to internal tissues through natural orifices or small external excisions are common practice in medicine. They are performed for problems as diverse as delivering stents through catheters, treating abdominal complications, and performing transnasal operations at the skull base in patients with neurological conditions. The ends of devices for such surgeries are highly flexible (or "articulated") to enable the visualization and specific manipulation of the surgical site in the target tissue. In the case of energy-delivering devices that allow surgeons to cut or dry (desiccate) tissues, and stop internal bleeds (coagulate) deep inside the body, a heat-generating energy source is added to the end of the device. However, presently available energy sources delivered via a fiber or electrode, such as radio frequency currents, have to be brought close to the target site, which limits surgical precision and can cause unwanted burns in adjacent tissue sections and smoke development.

Communication is more important than ever, with everything from college to CrossFit going virtual during the COVID-19 pandemic. Nobody understands this better than 2020 Marconi Prize recipient Andrea Goldsmith, who has spent her career making the wireless connections on which we rely more capable and stable. A pioneer of both theoretical and practical advances in adaptive wireless communications, Goldsmith spoke about her work on multiple-input and multiple-output (MIMO) channel performance limits, her new role as the incoming dean at Princeton University's School of Engineering and Applied Science, and what's next for networking. As an undergrad, you studied engineering at the University of California, Berkeley. What drew you to wireless communications?

Newly engineered slinky-like strain sensors for textiles and soft robotic systems survive the washing machine, cars and hammers. Think about your favorite t-shirt, the one you've worn a hundred times, and all the abuse you've put it through. You've washed it more times than you can remember, spilled on it, stretched it, crumbled it up, maybe even singed it leaning over the stove once. We put our clothes through a lot and if the smart textiles of the future are going to survive all that we throw at them, their components are going to need to be resilient. Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and the Wyss Institute for Biologically Inspired Engineering have developed an ultra-sensitive, seriously resilient strain sensor that can be embedded in textiles and soft robotic systems. The research is published in Nature.

Minimally invasive laparoscopic surgery, in which a surgeon uses tools and a tiny camera inserted into small incisions to perform operations, has made surgical procedures safer for both patients and doctors over the last half-century. Recently, surgical robots have started to appear in operating rooms to further assist surgeons by allowing them to manipulate multiple tools at once with greater precision, flexibility, and control than is possible with traditional techniques. However, these robotic systems are extremely large, often taking up an entire room, and their tools can be much larger than the delicate tissues and structures on which they operate. A collaboration between Wyss Associate Faculty member Robert Wood, Ph.D. and Robotics Engineer Hiroyuki Suzuki of Sony Corporation has brought surgical robotics down to the microscale by creating a new, origami-inspired miniature remote center of motion manipulator (the "mini-RCM"). The robot is the size of a tennis ball, weighs about as much as a penny, and successfully performed a difficult mock surgical task, as described in a recent issue of Nature Machine Intelligence. "The Wood lab's unique technical capabilities for making micro-robots have led to a number of impressive inventions over the last few years, and I was convinced that it also had the potential to make a breakthrough in the field of medical manipulators as well," said Suzuki, who began working with Wood on the mini-RCM in 2018 as part of a Harvard-Sony collaboration.

This itsy-bitsy robot can't climb up the waterspout yet but it can run, jump, carry heavy payloads and turn on a dime. Dubbed HAMR-JR, this microrobot developed by researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and Harvard's Wyss Institute for Biologically Inspired Engineering, is a half-scale version of the cockroach-inspired Harvard Ambulatory Microrobot or HAMR. About the size of a penny, HAMR-JR can perform almost all of the feats of its larger-scale predecessor, making it one of the most dexterous microrobots to date. "Most robots at this scale are pretty simple and only demonstrate basic mobility," said Kaushik Jayaram, Ph.D., a former postdoctoral fellow at SEAS and the Wyss Institute, and first author of the paper. "We have shown that you don't have to compromise dexterity or control for size."