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ELLIS Delft Talk by Javier Alonso-Mora

ELLIS Delft Talk by Javier Alonso-Mora 12 April 2022 16:00 (NOTE: the meeting moved from 5th -> 12th) This will be a hybrid meeting This meeting is open for all interested researchers. Motion Planning among Decision-Making Agents: Trajectory Optimization with Learned Cost Functions Abstract We move towards an era of smart cities, where autonomous vehicles will provide on-demand transportation while making our streets safer and mobile robots will coexist with humans. The motion plan of mobile robots and autonomous vehicles must, therefore, account for the interaction with other agents and consider that they are, as well, decision-making entities. For example, when humans drive a car, they are fully aware of their environment and how other drivers and pedestrians may react to their future actions. Towards this objective I will discuss several methods for motion planning and multi-robot coordination that leverage constrained optimization and reinforcement learning to achieve interactive behaviors with safety guarantees. Namely: using inverse reinforcement learning and social value estimation to achieve social behaviors; employing a learned policy to guide the motion planner in dense traffic scenarios or for information gathering; achieving social trajectories by learning a cost function from a dataset of human-driven vehicles; and learning to communicate the relevant information for multi-robot coordination. The methods are of broad applicability, including autonomous vehicles and aerial vehicles. Bio Javier Alonso-Mora is an Associate Professor at the Department of Cognitive Robotics of the Delft University of Technology, the director of the Autonomous Multi-robots Laboratory, a Principal Investigator at the Amsterdam Institute for Advanced Metropolitan Solutions and co-founder of The Routing Company. Previously, he was a Postdoctoral Associate at the Computer Science and Artificial Intelligence Lab (CSAIL) of the Massachusetts Institute of Technology. He received his Ph.D. degree in robotics from ETH Zurich, in partnership with Disney Research Zurich. He serves as associate editor for Springer Autonomous Robots, and has served as associate editor for the IEEE Robotics and Automation Letters, the Publications Chair for the IEEE International Symposium on Multi-Robot and Multi-Agent Systems 2021 and associate editor for ICRA, IROS and ICUAS. He is the recipient of several prizes and grants, including an ERC Starting Grant (2021), the ICRA Best Paper Award on Multi-robot Systems (2019), an Amazon Research Award (2019) and a talent scheme VENI award from the Netherlands Organisation for Scientific Research (2017). More info: https://www.autonomousrobots.nl/ To join this event, please contact Frans Oliehoek .

van Duijvenbode, J.R.

Profile TU Delft (2018 – current) Ph.D. candidate in Resource Engineering I obtained a MSc degree in the European Mining Course (EMC) from Delft University of Technology, Aalto University and RWTH Aachen. My master thesis was about: Development and Validation of Short-term Mine Planning Optimization Algorithms for a Sublevel Stoping Operation with Backfilling. Research PhD research into the behavioural Geology – Understanding how differences in geology influence metallurgical performance. The research topic consists of integrating collected information on metallurgical properties, directly or through proxies back into the resource model. The consideration of metallurgical costs is the only way forward to obtain truly optimized mining decisions, accounting for constraints and bottlenecks in the comminution circuit and chemical processing plant. This is important to better characterize metallurgical behavior of the plant feed, which allows for a morel optimal selection of process control settings. The envisioned solution will result in an increased recovery in combination with a lower utilization of energy and chemicals per tonne of processed material (lower environmental footprint). Consequently, overall OPEX will drop making lower grade ore economic while increasing the mineral resources that are available for conversion to ore reserves (lesser need to open up new mines). Moreover, a better characterization of mining blocks reduces the unintended processing of waste due to lower overall classification errors. Copromotor: Dr. M. Soleymani Shishvan Promotor(s): Dr. M. Buxton and Prof. Jan Dirk Jansen Jeroen van Duijvenbode PhD Candidate + 31 15 27 82262 J.R.vanDuijvenbode@tudelft.nl Faculty of Civil Engineering and Geosciences Building 23 Stevinweg 1 / PO-box 5048 2628 CN Delft / 2600 GA Delft Room number: 3.21

ELLIS Delft Talk by Guillaume Rongier

ELLIS Delft Talk by Guillaume Rongier Going beyond empirical relationships in geology: The example of total organic carbon 01 February 2022 16:00 Abstract While machine learning has a long history in geology, empirical relationships remain widely used. Through the example of total organic carbon (TOC), this talk will illustrate the close links between empirical relationships and machine learning, and the benefits of turning to machine learning. TOC is a measure of the proportion of organic carbon in rock samples typically gathered from boreholes. It can be used to assess the potential for hydrocarbons, understand rock mechanics, or assess reducing conditions for basin-hosted mineral systems, and is paramount when seeking to understand variations in paleo-environmental conditions. Since gathering and analyzing rock samples is expensive, empirical relationships have been developed to predict TOC from well logs, which are based on more widely available geophysical measurements into boreholes. Those empirical relationships come from geological and petrophysical principles implemented in mathematical models manually fitted to the data. This leads to several limitations, mainly poor generalization, inability to quantify uncertainties, time-consuming and subjective calibration that leads to reproducibility issues. But those empirical relationships can be rewritten as linear regressions, a simple change that solves many of the previous limitations. Turning to more advanced machine learning methods improves predictions by taking into account the non-linearity and variability in the data. Using the expert knowledge behind empirical relationships as input besides well logs improves the predictions as well: this shows that leveraging geological and petrophysical concepts through feature selection and engineering boosts machine learning performances. To join this event, please contact Frans Oliehoek .

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NWO grants funding for innovative research on physical experimental environments

How to move from experiment to mainstream? A consortium led by professor Tamara Metze, has been awarded a prestigious grant from the Netherlands Organisation for Scientific Research (NWO). In search for pathways toward more sustainable futures, Metze and her team will explore how various innovations in field labs such as The Green Village, in urban living labs such as Engy Lab South-East in Amsterdam, and in all sorts of citizens’ initiatives, can be mainstreamed and make more impact on sustainability transitions. Pilot paradox The project ‘From EXperiment to sustainable change: TRAnsformative methodologies for Innovation and learning’ (EXTRA) seeks to overcome a persistent “pilot paradox”. In this paradox, much experimentation takes place but long-term systemic impact remains difficult. Researchers together with all sorts of change makers will synthesise existing knowledge on how to mainstream, upscale, spread, broaden and deepen developed innovations. Tamara Metze: ‘I am excited to unravel what are effective ways of cocreation that lead to mainstreaming the positive changes made in experimental environments. We will figure out how learning and innovation can lead to lasting changes in regulations, policies, and financial systems and the biophysical environment.’ Tamara Metze Read the NWO press release Actionable tools The project is crucial for accelerating sustainability transitions. By refining methodologies for mission-driven experimentation and develop hands on tools for all sorts of change-makers, it will be easier to mainstream the sustainable lessons and innovations. ‘These tools will not only aid grassroots innovators but also influence institutional and organisational structures, ensuring that lessons learned from experiments are better anchored in policies, regulations, and organisations’, explains Metze. The project will employ a transdisciplinary action research approach, bringing together knowledge from various disciplines and policy domains. By co-creating solutions with public and private partners, the research will have an immediate impact. In the long term, the project aims to build a more efficient innovation ecosystem, contributing to more impactful and sustainable outcomes for both society and the environment. Projectpartners TU Delft, VU Amsterdam, Wageningen University & Research, Hogeschool van Amsterdam, Erasmus Universiteit Rotterdam, Hogeschool Rotterdam, The Green Village, AMS Institute; PBL Planbureau voor de Leefomgeving, WoonFriesland, Dijkstra Draisma, Provincie Noord-Holland, Ministerie van Binnenlandse Zaken, PRICE / Almere, BouwLab, Alliantie Samen Nieuw-West, Innovation Quarter.

Unusual waves grow way beyond known limits

Waves that come from multiple directions are extremer than extreme. These remarkable deep-sea waves can be four times steeper than what was previously imagined, as is shown in research by TU Delft and other universities that was published in Nature today. A long time ago, stories were told of mysterious rogue waves that materialised out of nowhere and could topple even the largest ships. These waves lost their mythical character when the first rogue wave was recorded at the Draupner platform in the North Sea. In 2018, Ton van den Bremer and his colleagues at the Universities of Edinburgh and Oxford managed to recreate the Draupner wave in the lab for the first time ever, and this opportunity to study freak waves closely produced unexpected insights. Multiple waves push up water New research by the research consortium now shows that these remarkable waves do not break when traditional theories hold they should, the secret behind which lies in how they arise. Ton van den Bremer, expert on fluid mechanics at TU Delft and led the study, explains: “When most people think of waves, they think of the rolling waves you’d find on a beach. The type of wave we studied occurs in open water and arises when waves coming from multiple directions come together. When these waves with a high directional spread converge, the water is pushed upwards, forming a partially standing wave. An example of this is known as a crossing wave. How crossing waves arise Under certain conditions at sea, waves from multiple directions occur. This can happen in a place where two seas meet, or where winds suddenly change direction, as in a hurricane. When waves from two directions meet, a cross wave occurs, provided their directions are far enough apart. The study also shows that the further apart the directions are, the higher the resulting cross-wave. Travelling waves break when they reach a certain limit, this is when they reach their maximum steepness. The study shows that waves with a multidirectional spreading can get as much as 80% steeper than this limit before they start breaking, which means they can get almost twice as high as ‘normal waves’ before they start to break. Travelling wave (l) and a wave with high directional spreading (r) Breaking waves that grow Next, the researchers found another highly unusual phenomenon that defies existing theories, a phenomenon that is unprecedented according to Van den Bremer: “Once a conventional wave breaks, it forms a white cap, and there is no way back. But when a wave with a high directional spreading breaks, it can keep growing.” The study shows that these enormous waves can grow to twice their original steepness while breaking, which is already twice bigger than the conventional limit. Together, the waves can grow four times steeper than previously thought possible. Damage to offshore structures The knowledge that multidirectional waves can become as much as four times larger than was deemed possible can help design safer marine structures. "The three-dimensionality of waves is often overlooked in the design of offshore wind turbines and other structures in general; our findings suggest this leads to designs that are less reliable", says Mark McAllister of the University of Oxford, who led the experiments and is now a senior scientist at Wood Thilsted. Innovative vertical sensors made it possible to take accurate 3D measurements of waves. Innovative 3D measurement method A 3D measurement method developed in the FloWave lab paved the way for these new insights. “Conventional 2D wave measurement methods weren’t up to the task”, Van den Bremer explains, which is why the research group designed a new way to create 3D wave measurements. Ross Calvert of the University of Edinburgh: “This is the first time we've been able to measure wave heights at such high spatial resolution over such a big area, giving us a much more detailed understanding of complex wave breaking behaviour." FloWave Ocean Energy Research Facility in Edinburgh. The circular basin has a diameter of 25 metres and can be used to generate waves from multiple directions. Header image by: Fabien Duboc