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BEGIN:VEVENT
DTSTAMP:20230831T095754Z
LOCATION:Davos
DTSTART;TZID=Europe/Stockholm:20230626T112000
DTEND;TZID=Europe/Stockholm:20230626T115000
UID:submissions.pasc-conference.org_PASC23_sess104@linklings.com
SUMMARY:Flash Poster Session - Part I
DESCRIPTION:Poster\n\nThe aim of this rapid-fire session is to allow poste
 r presenters to introduce the topic of their poster and motivate the audie
 nce to visit them at the poster session scheduled on the following day. Pr
 esenters will have 30 seconds to engage the audience.\n\nThe Flash Poster 
 Session is sponsored by the Royal Society of Chemistry Journals (https://w
 ww.rsc.org/). Winners of the poster competition will receive a voucher pri
 ze generously offered by the Digital Discovery (https://www.rsc.org/journa
 ls-books-databases/about-journals/digital-discovery/) and by PCCP (https:/
 /pubs.rsc.org/en/journals/journalissues/cp#!recentarticles&adv) during the
  award session scheduled on the last day of the conference.\n\nP05 - Ab In
 itio Modeling of Magnetite Surfaces for Plutonium Retention\n\nIn many cou
 ntries, thick steel casks are used for the containment of high-level radio
 active waste in deep geological repositories. In contact with pore-water, 
 steel corrodes forming mixed iron oxides, mainly magnetite at the surface.
  After tens of thousands of years, casks may breach allowing leachi...\n\n
 \nAnita S. Katheras and Konstantinos Karalis (University of Bern); Matthia
 s Krack (Paul Scherrer Institute); Andreas C. Scheinost (Helmholtz-Zentrum
  Dresden-Rossendorf); and Sergey V. Churakov (Paul Scherrer Institute, Uni
 versity of Bern)\n---------------------\nP23 - Evaluation of GPU Accelerat
 ed Machine Learning Algorithms for Energy Price Prediction\n\nThe Location
 al Marginal Pricing (LMP) mechanism is a way to calculate the cost of prov
 iding electricity to a specific point in the grid. Accurate forecasting of
  LMP is important for market participants such as power producers or finan
 cial institutions to optimize operations and bidding strategies. T...\n\n\
 nNaga Venkata Sai Jitin Jami (Università della Svizzera italiana, Friedric
 h-Alexander-Universität Erlangen-Nürnberg); Juraj Kardos and Olaf Schenk (
 Università della Svizzera italiana); and Harald Köstler (Friedrich-Alexand
 er-Universität Erlangen-Nürnberg)\n---------------------\nP10 - Applicatio
 n of Deep Learning and Reinforcement Learning to Boundary Control Problems
 \n\nMany scientific problems, such as fluid dynamics problems involving dr
 ag reduction, temperature control with some desired flow pattern, etc., re
 ly on optimal boundary control algorithms. These forward solves are perfor
 med for multiple simulation timesteps, and hence, a method to solve the bo
 undary c...\n\n\nZenin Easa Panthakkalakath and Juraj Kardoš (Università d
 ella Svizzera italiana) and Olaf Schenk (Università della Svizzera italian
 a, ETH Zurich)\n---------------------\nP29 - GT4Py: A Python Framework for
  the Development of High-Performance Weather and Climate Applications\n\nG
 T4Py is a Python framework for weather and climate applications simplifyin
 g the development and maintenance of high-performance codes in prototyping
  and production environments. GT4Py separates model development from hardw
 are architecture dependent optimizations, instead of intermixing both toge
 the...\n\n\nMauro Bianco and Till Ehrengruber (ETH Zurich / CSCS); Nicolet
 ta Farabullini and Abishek Gopal (ETH Zurich); Linus Groner and Rico Häuse
 lmann (ETH Zurich / CSCS); Peter Kardos, Samuel Kellerhals, and Magdalena 
 Luz (ETH Zurich); Christoph Müller (MeteoSwiss); Enrique G. Paredes (ETH Z
 urich / CSCS); Matthias Roethlin (MeteoSwiss); Felix Thaler and Hannes Vog
 t (ETH Zurich / CSCS); Benjamin Weber (MeteoSwiss); and Thomas C. Schulthe
 ss (ETH Zurich / CSCS)\n---------------------\nP08 - Analysis and Applicat
 ion of CNN to Improve Deterministic Optical Flow Nowcasting at DWD\n\nOpti
 cal flow based nowcasting is essential for several operational productions
  at DWD, including time critical warnings. Precipitation and radar reflect
 ivity nowcasts are produced every 5 minutes with a 5 minute stepping up to
  2h lead time. The method assumes stationarity of the input data. It is a 
 ...\n\n\nUlrich Friedrich (DWD)\n---------------------\nP12 - Building a P
 hysics-Constrained, Fast and Stable Machine Learning-Based Radiation Emula
 tor\n\nModeling the transfer of radiation through the atmosphere is a key 
 component of weather and climate models. The operational radiation scheme 
 in the Icosahedral Nonhydrostatic Weather and Climate Model (ICON) is ecRa
 d. The radiation scheme ecRad is accurate but computationally expensive. I
 t is operat...\n\n\nGuillaume Bertoli and Sebastian Schemm (ETH Zurich) an
 d Firat Ozdemir, Fernando Perez Cruz, and Eniko Szekely (Swiss data scienc
 e center)\n---------------------\nP06 - Accurate Electronic Properties and
  Intercalation Voltages of Li-Ion Cathode Materials from Extended Hubbard 
 Functionals\n\nThe design of novel cathode materials for Li-ion batteries 
 requires accurate first-principles predictions of their properties. Densit
 y-functional theory (DFT) with standard (semi-)local functionals fails due
  to the strong self-interaction errors of partially filled d shells of tra
 nsition-metal (TM) ...\n\n\nIurii Timrov, Francesco Aquilante, and Michele
  Kotiuga (EPFL); Matteo Cococcioni (University of Pavia); and Nicola Marza
 ri (EPFL)\n---------------------\nP27 - GPU-Accelerated Modelling of Green
 house Gases and Air Pollutants in ICON with OpenACC\n\nReleasing excess gr
 eenhouse gases into the atmosphere is the major cause of its natural compo
 sition alternation and climate change. Computational modelling of the atmo
 spheric chemistry and transport processes has played a vital role in enhan
 cing our understanding of such complex phenomena and helped...\n\n\nArash 
 Hamzehloo and Dominik Brunner (Empa)\n---------------------\nP04 - A Resea
 rch Software Engineering Workflow for Computational Science and Engineerin
 g\n\nWe present a Research Software Engineering (RSE) workflow for develop
 ing research software in Computational Science and Engineering (CSE) in un
 iversity research groups. Their members have backgrounds from different sc
 ientific disciplines and often lack education in RSE. Research software de
 velopment...\n\n\nMoritz Schwarzmeier, Tomislav Marić, Tobias Tolle, Jan-P
 atrick Lehr, Ioannis Pappagianidis, Benjamin Lambie, Dieter Bothe, and Chr
 istian Bischof (TU Darmstadt)\n---------------------\nP24 - Geodynamo Simu
 lations in a Full Sphere\n\nAlthough the geomagnetic field exists since ab
 out 4 Gyr, recent estimates for the formation of the Earth's inner core go
  back no further than 500 Myr to 1 Gyr. Here we run rapidly rotating dynam
 os in a full sphere geometry, representative of the Earth's dynamo before 
 the nucleation of the inner core...\n\n\nFabian Burmann, Jiawen Luo, Phili
 ppe David Marti, and Andrew Jackson (ETH Zurich)\n---------------------\nP
 01 - A Language-Interoperable C++-Based Memory-Manager for the ICON Climat
 e and Weather Prediction Model\n\nHPC machines now use accelerators such a
 s GPUs. In addition, CPUs themselves now feature many cores as well as spe
 cial fast memory, like the Fujistu A64FX and Intel Sapphire Rapids. These 
 rapid changes create important challenges for simulation codes to accommod
 ate different parallel programming mod...\n\n\nClaudius Holeksa (Karlsruhe
  Institute of Technology), Ralf Müller and Jörg Behrens (German Climate Co
 mputing Centre), Florian Prill (DWD), Christopher Bignamini and Will Sawye
 r (ETH Zurich / CSCS), Xavier Lapillonne (MeteoSwiss), Sergey Kosukhin and
  Daniel Klocke (Max Planck Institute for Meteorology), Terry Cojean and Ye
 n-Chen Chen (Karlsruhe Institute of Technology), Hartwig Anzt (University 
 of Tennessee), and Claudia Frauen (German Climate Computing Centre)\n-----
 ----------------\nP14 - Closing the Gap: Aligning Developers’ Expectations
  and Users’ Practices in Cloud Computing Infrastructure\n\nThere are often
  discrepancies between the uses that infrastructure developers envision fo
 r their technology and the way they are implemented in reality. We report 
 on this gap between expectation and practice based on our ongoing study of
  the user-experience on a national cyberinfrastructure system f...\n\n\nTa
 manna Motahar, Johanna Cohoon, Kazi Sinthia Kabir, and Jason Wiese (Univer
 sity of Utah)\n---------------------\nP16 - Denoising Electronic Signals f
 rom Particle Detectors Using a Flexible Deep Convolutional Autoencoder\n\n
 In this work, we present the use of a deep convolutional autoencoder to de
 noise signals from particle detectors. The study of rare particle interact
 ions is crucial in advancing our understanding of the Universe. However, t
 he presence of electronic noise makes signal events difficult to distingui
 sh f...\n\n\nMark Anderson, Noah Rowe, and Tianai Ye (Queen's University)\
 n---------------------\nP20 - Docker Container in DWD's Seamless INtegrate
 d FOrecastiNg sYstem (SINFONY)\n\nAt Deutscher Wetterdienst (DWD), the SIN
 FONY project has been set up to develop a seamless ensemble prediction sys
 tem for convective-scale forecasting with forecast ranges of up to 12 hour
 s. It combines Nowcasting (NWC) techniques with numerical weather predicti
 on (NWP) in a seamless way. So far NWC...\n\n\nMatthias Zacharuk (DWD)\n--
 -------------------\nP02 - A Massively Parallel Approach to Forecasting El
 ectricity Prices\n\nWith the ongoing energy crisis in Europe, accurate for
 ecasting of electricity price levels and volatility is essential to planni
 ng grid operations and protecting consumers from extreme prices. We presen
 t how massively parallel stochastic optimal power flow models can be deplo
 yed on modern many-core ...\n\n\nTimothy Holt (Università della Svizzera i
 taliana, Oak Ridge National Laboratory)\n---------------------\nP25 - Gink
 go — A High-Performance Portable Numerical Linear Algebra Software\n\nNume
 rical linear algebra building blocks are used in many modern scientific ap
 plications codes. Ginkgo is an open-source numerical linear algebra softwa
 re designed around the principles of portability, flexibility, usability, 
 and performance. The Ginkgo library is integrated into the deal.II, MFEM, 
 ...\n\n\nTerry Cojean and Isha Aggarwal (Karlsruhe Institute of Technology
 ); Natalie Beams and Hartwig Anzt (University of Tennessee); and Yen-Chen 
 Chen, Thomas Grützmacher, Fritz Göbel, Marcel Koch, Gregor Olenik, Pratik 
 Nayak, Tobias Ribizel, and Yu-Hsiang Tsai (Karlsruhe Institute of Technolo
 gy)\n---------------------\nP18 - Directive-Based, Fortran/C++ Interoperab
 le Approach to GPU Offloading of the High Performance Gyrokinetic Turbulen
 ce Code GENE-X\n\nThe achievement of high plasma confinement is the key to
  realize commercially attractive energy production by magnetic confinement
  fusion (MCF) devices. Turbulence plays a significant role in maintaining 
 the plasma confinement within MCF devices. The GENE-X code is based on an 
 Eulerian (continuum) a...\n\n\nJordy Trilaksono, Philipp Ulbl, and Andreas
  Stegmeir (Max Planck Institute for Plasma Physics) and Frank Jenko (Max P
 lanck Institute for Plasma Physics, University of Texas at Austin)\n------
 ---------------\nP26 - Global Sensitivity Analysis of High-Dimensional Mod
 els with Correlated Inputs\n\nGlobal sensitivity analysis is an important 
 tool used in many domains of computational science to either gain insight 
 into the mathematical model and interaction of its parameters or study the
  uncertainty propagation through the input-output interactions. This works
  introduces a comprehensive framew...\n\n\nJuraj Kardos and Olaf Schenk (U
 niversità della Svizzera italiana) and Derek Groen and Diana Suleimenova (
 Brunel University London)\n---------------------\nP17 - Detecting Financia
 l Fraud with Graph Neural Networks\n\nDetecting financial fraud is a chall
 enging classification problem that entails the discovery of suspicious pat
 terns in large-scale and time evolving data. Traditionally, financial inst
 itutions have been relying on rule-based methods to identify suspicious ac
 counts, with such approaches becoming inef...\n\n\nJulien Schmidt, Dimosth
 enis Pasadakis, and Olaf Schenk (Università della Svizzera italiana)\n----
 -----------------\nP07 - Addressing Exascale Challenges for Numerical Algo
 rithms of Strongly Correlated Lattice Models\n\nStrongly Correlated Lattic
 e Models play an important role for our understanding of Quantum Magnetism
 , High-Tc superconductors, and also Quantum Simulators built from cold ato
 ms, trapped ions, Rydberg atoms, or superconducting qubits. Wave function 
 based numerical algorithms, such as Exact Diagonaliz...\n\n\nSamuel Gozel 
 (Paul Scherrer Institute) and Andreas M. Läuchli (Paul Scherrer Institute,
  EPFL)\n---------------------\nP09 - Analyzing Physics-Informed Neural Net
 works for Solving Classical Flow Problems\n\nThe application of Neural Net
 works (NNs) has been extensively investigated for fluid dynamic problems. 
 A specific form of NNs are Physics-Informed Neural Networks (PINNs), which
  incorporate physics-based embeddings to account for physical laws. In thi
 s work, the performance of PINNs is compared to t...\n\n\nRishabh Puri (Fo
 rschungszentrum Jülich); Mario Rüttgers (Forschungszentrum Jülich, RWTH Aa
 chen University); and Rakesh Sarma and Andreas Lintermann (Forschungszentr
 um Jülich)\n---------------------\nP11 - Bridging the Language Gap: Classe
 s for C++/Fortran Interoperability\n\nFortran and C++ remain popular langu
 ages for high-performance scientific computing. Interoperation of these tw
 o languages is of great interest; be it to take advantage of a mature ecos
 ystem of libraries, or for coupling individual simulation codes into large
 r multi-scale or multi-physics application...\n\n\nIvan Pribec (Leibniz Su
 percomputing Centre)\n---------------------\nP15 - Compressing Multidimens
 ional Weather and Climate Data into Neural Networks\n\nWeather and climate
  simulations produce petabytes of high-resolution data that are later anal
 yzed by researchers in order to understand climate change or severe weathe
 r. We propose a new method of compressing this multidimensional weather an
 d climate data: a coordinate-based neural network is traine...\n\n\nLangwe
 n Huang and Torsten Hoefler (ETH Zurich)\n---------------------\nP03 - A N
 ovel Stochastic Parameterization for Lagrangian Modeling of Atmospheric Ae
 rosol Transport\n\nIn recent years, it has become clear that the behavior 
 of atmospheric aerosols has a non-negligible effect on radiative forcing w
 ithin Earth's climate and the computational models that simulate it [Carsl
 aw, et al., Nature, 2013]. Thus, we must obtain descriptive aerosol models
  that are also predicti...\n\n\nMichael Schmidt (Sandia National Laborator
 ies)\n---------------------\nP13 - Calculation of the Maximally Localized 
 Wannier Functions in the SIRIUS Library\n\nElectronic properties of the ma
 terials are one of the major line of research for studying existing and di
 scovering novel materials. DFT+U and Koopman spectral functionals constitu
 te a good approach for correcting the DFT band structure, which is usually
  not good for the prediction of some of the pro...\n\n\nGiovanni Consalvo 
 Cistaro (EPFL), Nicola Colonna (Paul Scherrer Institute), Iurii Timrov (EP
 FL), Anton Kozhevnikov (ETH Zurich / CSCS), and Nicola Marzari (EPFL)\n\nS
 ession Chair: Elaine M. Raybourn (Sandia National Laboratories)
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