Researchers at Paderborn University in Germany have built a robot that can knock a ball into a hole using a club on a putting green on most attempts. Annika Junker, Niklas Fittkau, Julia Timmermann and Ansgar Trächtler have published a paper on the arXiv preprint server describing their robot and its performance. Golf is a notoriously difficult sport--professionals and amateurs alike spend countless hours attempting to improve their game. One of the most difficult parts of the game is putting the ball into the hole. Much of the difficulty lies in the combination of factors at play--the height of the grass and its roughness, the amount of wind and degree of humidity, and worst of all, the terrain.

This work presents a physics-informed neural network (PINN) based framework to model the strain-rate and temperature dependence of the deformation fields in elastic-viscoplastic solids. To avoid unbalanced back-propagated gradients during training, the proposed framework uses a simple strategy with no added computational complexity for selecting scalar weights that balance the interplay between different terms in the physics-based loss function. In addition, we highlight a fundamental challenge involving the selection of appropriate model outputs so that the mechanical problem can be faithfully solved using a PINN-based approach. We demonstrate the effectiveness of this approach by studying two test problems modeling the elastic-viscoplastic deformation in solids at different strain rates and temperatures, respectively. Our results show that the proposed PINN-based approach can accurately predict the spatio-temporal evolution of deformation in elastic-viscoplastic materials.

We present a differentiable greenhouse simulation model based on physical processes whose parameters can be obtained by training from real data. The physics-based simulation model is fully interpretable and is able to do state prediction for both climate and crop dynamics in the greenhouse over very a long time horizon. The model works by constructing a system of linear differential equations and solving them to obtain the next state. We propose a procedure to solve the differential equations, handle the problem of missing unobservable states in the data, and train the model efficiently. Our experiment shows the procedure is effective. The model improves significantly after training and can simulate a greenhouse that grows cucumbers accurately.

Abstract--We are developing a self-learning mechatronic golf robot using combined data-driven and physics-based methods, to have the robot autonomously learn to putt the ball from an arbitrary point on the green. Apart from the mechatronic control design of the robot, this task is accomplished by a camera system with image recognition and a neural network for predicting the stroke velocity vector required for a successful hole-in-one. To minimize the number of time-consuming interactions with the real system, the neural network is pretrained by evaluating basic physical laws on a model, which approximates the golf ball dynamics on the green surface in a data-driven manner. Thus, we demonstrate the synergetic combination of data-driven and physics-based methods on the golf robot as a mechatronic example system. With the aid of autonomous robots, the everyday life of many people should be made easier in the near future, e.g., by For this, a prudent action of the autonomous robot is essential.

Julia (https://julialang.org/) is a generic programming language designed for high-performance computing. It solves the "two language problem" of scientific computing. Julia is dynamically typed like scripting language such as Python and can be compiled into native machine code. Besides, composability via multiple dispatches makes Julia ideal for integration across packages. SciML (https://sciml.ai/) is an open-source software for scientific machine learning based on the Julia language that combines machine learning and scientific computing by integrating numerous standalone packages.

Climate change and increasing resource scarcity are challenges that Europe needs to face in the coming decades. All this has a direct impact on air transport, which is struggling to maintain its performance and competitiveness while ensuring a development focused on sustainable mobility. Research and innovation are essential to maintain the capabilities of the aviation industry, driven by the rise of new markets and new competitors as a result of globalization. A new longterm vision for the aeronautics sector is essential to ensure its successful advancement. In this line, new requirements for the future aviation industry have been defined by the ACARE Flightpath 2050, a Group of Recognized Personalities in the aeronautic sector, including stakeholders from the aeronautics industry, air traffic management, airports, airlines, energy providers and the research community. Aeronautics and air transport comprises both: air vehicle and system technology.

Across engineering and scientific disciplines, machine learning is the main method for analyzing and identifying patterns in big data and making informed decisions around that data. Recently, a new area within artificial intelligence called scientific machine learning has emerged, which introduces physics laws into machine learning models. Scientific machine learning combines the areas of artificial intelligence and scientific computation. Because scientific machine learning algorithms are informed and constrained by physics laws, they do not rely only on data and can even make predictions where there is no data. However, there has been little work on probabilistic methods in scientific machine learning, meaning that current algorithms cannot model uncertainty in the data or the physics.

Scientific machine learning (SciML) is a field of increasing interest in several different application fields. In an optimization context, SciML-based tools have enabled the development of more efficient optimization methods. However, implementing SciML tools for optimization must be rigorously evaluated and performed with caution. This work proposes the deductions of a robustness test that guarantees the robustness of multiobjective SciML-based optimization by showing that its results respect the universal approximator theorem. The test is applied in the framework of a novel methodology which is evaluated in a series of benchmarks illustrating its consistency. Moreover, the proposed methodology results are compared with feasible regions of rigorous optimization, which requires a significantly higher computational effort.

Some of the world's biggest companies are going all-in on the metaverse. One you may not know about is Ansys, a US public company that makes engineering simulation software and has been around since 1970. Dr. Prith Banerjee is its Chief Technology Officer, and I spoke to him last week about his vision for the metaverse -- and specifically, why he thinks the metaverse can't reach its full potential without "optimum physics-based modeling and simulation." Ansys, it turns out, already has a number of partnerships with companies building the metaverse -- including global telecoms companies, microchip and GPU manufacturers, data center and storage companies, and "all the cloud providers," according to Banerjee. He said that Ansys provides a mix of hardware and software expertise to these customers; everything from building hardware to designing a structural electromagnetics system.

Algorithmic Game theory is about strategic interactions among intelligent individuals, and mechanism design is about creating effective incentives in economic settings. Together, they're fascinating ways to understand human behavior and the challenges of designing and building systems. Algorithmic game theory (AGT) is a way of analyzing social interactions that use mathematical models to predict the strategies that individuals will adopt in any given situation. A simple game theory model can predict human behavior in many situations. But the surprising thing is that this same model can also explain the complex, self-organizing systems that power the World Wide Web.