Automated decisions made by self-moving vehicles require networking with the surrounding infrastructure and between the moving vehicles. CASUS’ use of machine learning methods in combination with behavior models and sensor data will create a digital image of the current surroundings for navigating the vehicle.
Automated decisions made by self-moving vehicles require networking with the surrounding infrastructure and between the moving vehicles. CASUS’ use of machine learning methods in combination with behavior models and sensor data will create a digital image of the current surroundings for navigating the vehicle. This enables, for example, better decision-making about the behavior of other vehicles.
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CASUS position
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Center for Advanced Systems Understanding
Conrad-Schiedt-Straße 20
D-02826 Görlitz
Automated decisions made by self-driving vehicles require networking with the surrounding infrastructure and between individual road users. CASUS’ use of machine learning methods in combination with behavior models and sensor data will create a digital image of the current surroundings.
T. Gawne, Z. A. Moldabekov, O. S. Humphries, K. Appel, C. Bähtz, V. Bouffetier, E. Brambrink, A. Cangi, S. Göde, Z. Konôpková, M. Makita, M. Mishchenko, M. Nakatsutsumi, K. Ramakrishna, L. Randolph, S. Schwalbe, J. Vorberger, L. Wollenweber, U. Zastrau, T. Dornheim, T. R. Preston Phys. Rev. B 109, L241112 (2024)
Using an ultrahigh resolution (Δ𝐸∼0.1eV) setup to measure electronic features in x-ray Thomson scattering (XRTS) experiments at the European XFEL in Germany, we have studied the collective plasmon excitation in aluminium at ambient conditions, which we can measure very accurately even at low momentum transfers. As a result, we can resolve previously reported discrepancies between ab initio time-dependent density functional theory simulations and experimental observations..
H. Tahmasbi, K. Ramakrishna, M. Lokamani, A. Cangi Phys. Rev. Mater. 8, 033803 (2024)
We created a computational workflow to analyze the potential energy surface (PES) of materials using machine-learned interatomic potentials in conjunction with the minima hopping algorithm. We demonstrate this method by producing a versatile machine-learned interatomic potential for iron hydride via a neural network using an iterative training process to explore its energy landscape under different pressures…
V. Martinetto, K. Shah, A. Cangi, A. Pribram-Jones Mach. Learn.: Sci. Technol. 5 015050 (2024)
Electronic structure theory calculations offer an understanding of matter at the quantum level, complementing experimental studies in materials science and chemistry. One of the most widely used methods, density functional theory, maps a set of real interacting electrons to a set of fictitious non-interacting electrons that share the same probability density…
