| 1. |
- Reifarth, R., et al.
(författare)
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Nuclear astrophysics with radioactive ions at FAIR
- 2016
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Ingår i: Journal of Physics: Conference Series. - : IOP Publishing. - 1742-6588 .- 1742-6596. ; 665:1
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Konferensbidrag (refereegranskat)abstract
- The nucleosynthesis of elements beyond iron is dominated by neutron captures in the s and r processes. However, 32 stable, proton-rich isotopes cannot be formed during those processes, because they are shielded from the s-process flow and r-process beta-decay chains. These nuclei are attributed to the p and rp process. For all those processes, current research in nuclear astrophysics addresses the need for more precise reaction data involving radioactive isotopes. Depending on the particular reaction, direct or inverse kinematics, forward or time-reversed direction are investigated to determine or at least to constrain the desired reaction cross sections. The Facility for Antiproton and Ion Research (FAIR) will offer unique, unprecedented opportunities to investigate many of the important reactions. The high yield of radioactive isotopes, even far away from the valley of stability, allows the investigation of isotopes involved in processes as exotic as the r or rp processes.
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| 2. |
- Ferrari, A. C., et al.
(författare)
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Science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems
- 2015
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Ingår i: Nanoscale. - : Royal Society of Chemistry (RSC). - 2040-3372 .- 2040-3364. ; 7:11, s. 4598-4810
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Tidskriftsartikel (refereegranskat)abstract
- We present the science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems, targeting an evolution in technology, that might lead to impacts and benefits reaching into most areas of society. This roadmap was developed within the framework of the European Graphene Flagship and outlines the main targets and research areas as best understood at the start of this ambitious project. We provide an overview of the key aspects of graphene and related materials (GRMs), ranging from fundamental research challenges to a variety of applications in a large number of sectors, highlighting the steps necessary to take GRMs from a state of raw potential to a point where they might revolutionize multiple industries. We also define an extensive list of acronyms in an effort to standardize the nomenclature in this emerging field.
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| 3. |
- Bernal, Ximena E., et al.
(författare)
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Empowering Latina scientists
- 2019
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 363:6429, s. 825-826
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 4. |
- Biferale, L., et al.
(författare)
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Zermelo's problem: Optimal point-to-point navigation in 2D turbulent flows using reinforcement learning
- 2019
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Ingår i: Chaos. - : AIP Publishing. - 1054-1500. ; 29:10
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Tidskriftsartikel (refereegranskat)abstract
- To find the path that minimizes the time to navigate between two given points in a fluid flow is known as Zermelo's problem. Here, we investigate it by using a Reinforcement Learning (RL) approach for the case of a vessel that has a slip velocity with fixed intensity, Vs, but variable direction and navigating in a 2D turbulent sea. We show that an Actor-Critic RL algorithm is able to find quasioptimal solutions for both time-independent and chaotically evolving flow configurations. For the frozen case, we also compared the results with strategies obtained analytically from continuous Optimal Navigation (ON) protocols. We show that for our application, ON solutions are unstable for the typical duration of the navigation process and are, therefore, not useful in practice. On the other hand, RL solutions are much more robust with respect to small changes in the initial conditions and to external noise, even when V-s is much smaller than the maximum flow velocity. Furthermore, we show how the RL approach is able to take advantage of the flow properties in order to reach the target, especially when the steering speed is small. Published under license by AIP Publishing.
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| 5. |
- Buzzicotti, M., et al.
(författare)
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Optimal control of point-to-point navigation in turbulent time dependent flows using reinforcement learning
- 2021
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Ingår i: CEUR Workshop Proceedings. - 1613-0073.
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Konferensbidrag (refereegranskat)abstract
- We present theoretical and numerical results concerning the problem to find the path that minimizes the time to navigate between two given points in a complex fluid and under realistic navigation constraints. We contrast deterministic Optimal Navigation (ON) control with stochastic policies obtained by Reinforcement Learning (RL) algorithms. We show that Actor-Critic RL algorithms are able to find quasi-optimal solutions in the presence of either time-independent or chaotically evolving flow configurations. For our application, ON solutions develop unstable behaviour within the typical duration of the navigation process, and are therefore not useful in practice. The explored setup consists of using a constant propulsion speed to navigate a turbulent flow. Based on a discretized phase-space the propulsion direction is adjusted with the aim to minimize the time spent to reach the target. Our approach can be generalized to other set-ups, for example unmanned navigation with minimal energy consumption under imperfect environmental forecast or with different models for the moving vessel.
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