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<title>Enrico Camporeale | Centrum Wiskunde & Informatica</title>
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<li><a href="index.html">Home</a></li>
<li><a href="research.html"><font color="red">Research</font></a></li>
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<h2>Enrico Camporeale</h2>
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<h2>Research projects</h2>
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<h4><a href="#ML">Machine Learning for Space Physics</a></h4>
<p>Deep Learning and Gaussian Processes</p>
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<p>Can we use solar images and in-situ satellite data to train models that can predict Space Weather events? Can Machine Learning help in discovering new physics? The first book on the topic <a href="https://www.elsevier.com/books/machine-learning-techniques-for-space-weather/camporeale/978-0-12-811788-0" target="_blank" >here.</a> </p>
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<h4><a href="#SW_turbulence">Solar Wind turbulence</a></h4>
<p>Kinetic physics of turbulent dissipation regime</p>
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<p> How does turbulent energy dissipate in a collisionless plasma? What is the role and the interplay between coherent structures, magnetic reconnection, wave-particle interaction at sub-ion scales? </p>
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<h4><a href="#UQ">Uncertainty Quantification</a></h4>
<p>Adaptive Sampling, Markov Chain Monte Carlo</p>
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<p>What is the best strategy to sample input parameters from an high-dimensional space? How to interpret the result of a deterministic simulation in a probabilistic setting?</p>
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<h4><a href="#vlasov">Computational Methods for Vlasov Equation</a></h4>
<p>Spectral methods, Particle-in-Cell</p>
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<p>How to combine the fluid and kinetic description in an optimal way, with low computational cost? </p>
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<h2>Machine Learning for Space Physics</h2>
<p><a href="./mlprojects.html" target="_blank">Coupling physics-based simulations with Artificial Intelligence</a></p>
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I am leading a team that is pioneering the use of Machine Learning in Space Physics on several fronts.</br> In our team, we exploit a unique combination of expertise in Space Physics, Computational Physics, Machine Learning and Data Analysis.</br>
We aim at enhancing the current state-of-the-art simulations for Space Weather, by using prior knowledge gathered from historical satellite data. Several Machine Learning techniques will be used for data-mining, classification, and regression. The long-term objective of the project is the creation of a portfolio of data-enhanced reduced models, along with automated rules for model selection.</p>
All our codes and data are publicly available.
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<h4>Sponsors and Grants</h4>
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<a href="https://www.cwi.nl/news/2017/vidi-grant-for-enrico-camporeale-s-space-weather-research" target="_blank"><img alt="Alt Text" src="images/NWO_logo.jpeg" style="width:10%"/></a>
<a href="https://project.inria.fr/inriacwi/projects/mdg-tao/" target="_blank"><img alt="Alt Text" src="images/inria.jpg" style="width:100"/><a>
<a href="http://www.aida-space.eu/" target="_blank"> <img alt="Alt Text" src="images/AIDA_logo.png" style="width:10%"/></a>
<a href="https://projectescape.eu" target="_blank"> <img alt="Alt Text" src="images/Escape_logo.png" style="width:20%"/></a>
<a href="https://www.soars.ucar.edu/" target="_blank"> <img alt="Alt Text" src="images/SOARS_logo.jpg" width=150/></a>
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<p>NWO is CWI is the Dutch Research Council. INRIA is the French Institute for Research in Computer Science and Automation. AIDA and ESCAPE are Horizon-2020 projects funded by the European Commission. SOARS, Significant Opportunities in Atmospheric Research and Science, is an undergraduate-to-graduate bridge program sponsored by NSF.</p>
More details on our Machine Learning projects are available <a href="mlprojects.html"> here</a>.</br>
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<h2>Uncertainty Quantification</h2>
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<p><span ><img src="images/UQ_book.jpg" width=15% align="left" alt="" style="margin:0px 50px 20px 0px" alt="" /></span>
Understanding how uncertainties associated to input parameters propagate through a non-linear simulation and produce a stochastic output form the core of a subject called Uncertainty Quantification. I am interested, in particular, in the so-called non-intrusive approach, where one can use a black-box simulation model and run an ensemble of such simulations to produce a probabilistic output. A major research question is then how to optimally sample the input parameter space, and whether one can hope to do any better than (quasi-) Monte Carlo methods. I have introduced a new adaptive sampling method that consistently beats Monte-Carlo also for very large dimensions.<br> </br>
Relevant publications:
<ul>
<li><u>E. Camporeale </u>, Chu, X., Agapitov, O. V., Bortnik, J. (2019)<br> <b>On the Generation of Probabilistic Forecasts From Deterministic Models</b>,<i> Space Weather</i>,17(3), 455-475
<font size="2.5"><a href="https://homepages.cwi.nl/~camporea/papers/2018SW002026.pdf" target="_blank">Download full text</a></font>
<li> <u>Camporeale, E. </u>, Agnihotri, A., Rutjes, C. (2017)<br>
<b>Adaptive selection of sampling points for uncertainty quantification</b>, <i> Int. J. Uncertainty Quant.</i>
<font size="2.5"><a href="https://homepages.cwi.nl/~camporea/papers/1612.07827.pdf" target="_blank">Download full text</a></font>
<li> <u>Camporeale, E.</u>, Y. Shprits, M. Chandorkar, A. Drozdov, S. Wing (2016) <br>
<b> On the propagation of uncertainties in radiation belt simulations </b>, <i> Space Weather</i>
<font size="2.5"><a href="https://homepages.cwi.nl/~camporea/papers/camporeale_SW16_for_arxiv.pdf" target="_blank"> Download full text</a></font>
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<h2>Solar Wind Turbulence</h2>
<p>Kinetic physics of turbulent dissipation regime</p>
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Models of Solar Wind acceleration and coronal heating predict that the flux of energetic particles moving away from the Sun has to undergo wave-particle interactions in order to locally gain energy and not cool adiabatically, as it is observed. The mechanism behind those interactions is still not clear; in the "turbulent cascade" picture the typical scale of those phenomena would be within the small dissipation regime. That means that any fluid approach would miss important informations about the thermal properties of the particles, and a kinetic approach is needed.
I am investigating the physics governing such phenomena by computer simulations (mostly Particle-in-cell).</br>
Relevant publications:<br>
<ul>
<li><u>E. Camporeale</u>, L. Sorriso-Valvo, F. Califano, A. Retinò (2018)<br>
<b> Coherent structures and spectral energy transfer in turbulent plasma: a space-filter approach</b>, <i> Phys. Rev. Lett. </i> 120,125101
<font size="2.5"><a href="https://arxiv.org/pdf/1711.00291" target="_blank"> Download full text</a></font>
<li> <u>Camporeale, E. </u> & Burgess, D (2017) <br>
<b>Comparison of linear modes in kinetic plasma models</b>, <i> J. Plasma Phys.</i>, 83
<font size="2.5"><a href="https://homepages.cwi.nl/~camporea/papers/comparison_of_linear_modes_in_kinetic_plasma_models.pdf" target="_blank"> Download full text</a></font>
<li> A. Vaivads et al. (2016) <br>
<b> Turbulence Heating ObserveR – satellite mission proposal </b>, <i> J. Plasma Phys.</i>, 82
<font size="2.5"><a href="https://homepages.cwi.nl/~camporea/papers/thor.pdf" target="_blank">Download full text</a></font>
<li>Vasconez, C. L., Valentini, F., <u>Camporeale, E.</u>, & Veltri, P. (2014)<br>
<b>Vlasov simulations of kinetic Alfven waves at proton kinetic scales</b>. <br>
<i>Phys. Plasmas</i>, 21(11), 112107. <font size="2.5"> <a href="papers/1.4901583.pdf" target="_blank"> Download full text </a> </font>
<li>Haynes, C. T., Burgess, D., & <u>Camporeale, E.</u> (2014).<br>
<b> Reconnection and electron temperature anisotropy in sub-proton scale plasma turbulence.</b> <br>
<i>Astrophys. J. </i> 783(1), 38.
<font size="2.5"> <a href="http://arxiv.org/pdf/1304.1444v2" target="_blank"> Download full text </a> </font>
<li><u>Camporeale, E.</u>, D. Burgess (2011)<br>
<b> The dissipation of solar wind turbulent fluctuations at electron scales </b><br>
<i> Astrophys. J. </i>, 730 114
<font size="2.5"> <a href="http://iopscience.iop.org/0004-637X/730/2/114/pdf/0004-637X_730_2_114.pdf" target="_blank"> Download full text </a> </font>
<li><u>Camporeale, E.</u>, T. Passot, D. Burgess (2010)<br>
<b> Implications of a Non-Modal Linear Theory for the Marginal
Stability State and the Dissipation of Fluctuations in the Solar Wind </b><br>
<i> Astrophys. J. </i> 715, 260-270
<font size="2.5"> <a href="papers/apj_715_1_260.pdf" target="_blank"> Download full text </a> </font>
<li><u>Camporeale, E.</u>, D. Burgess (2010)<br>
<b> Electron temperature anisotropy in an expanding plasma: Particle in Cell simulations </b><br>
<i> Astrophys. J. </i> 710, 1848-1856
<font size="2.5"> <a href="papers/apj_710_2_1848.pdf" target="_blank"> Download full text </a> </font>
<li><u>Camporeale, E.</u> and D. Burgess (2008)<br>
<b> Electron firehose instability: Kinetic linear theory and two-dimensional particle-in-cell simulations</b><br>
<i>J. Geophys. Res.</i>, 113, A07107, doi:10.1029/2008JA013043
<font size="2.5"> <a href="papers/JGR2008.pdf" target="_blank"> Download full text </a> </font>
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<h2>Vlasov Equation</h2>
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I have a strong expertise in Particle-in-Cell (PIC) methods, which are used by the overwhelming majority of plasma physicist to study the kinetic properties of hot plasmas.
One of the PIC code that I have more intensively used is based on the implicit moment method. It has the feature of relaxing some stability constraint, with respect to standard PIC codes.<br>
Unfortunately, PIC suffers from noise and cannot afford an high resolution in phase space. Therefore, for some physical problems (such as involving energy dissipation), one has to use more expensive methods aimed at directing solving Vlasov equation. I am interested in novel computational methods to solve the Vlasov equation.<br>
A promising technique that I am investigating is a Galerkin spectral method employing Hermite functions.<br/><br>
Relevant publications:<br>
<ul>
<li> Pezzi, O., <u>E. Camporeale</u>, F. Valentini (2016)<br>
<b>Collisional effects on the numerical recurrence in Vlasov-Poisson simulations</b>, <i> Phys. Plasmas</i>, 23, 022103
<font size="2.5"><a href="https://homepages.cwi.nl/~camporea/papers/1.4940963.pdf" target="_blank">Download full text</a></font>
<li> <u>Camporeale, E.</u>, G.L. Delzanno, B.K Bergen, J.D. Moulton (2016)<br>
<b>On the velocity space discretization for the
Vlasov-Poisson system: comparison between implicit
Hermite spectral and Particle-in-Cell methods.</b>
<i> Comp. Phys. Comm.</i>, 198, 47-58
<font size="2.5"><a href="https://homepages.cwi.nl/~camporea/papers/camporeale_cpc_15.pdf" target="_blank">Download full text</a></font>
<li>Tronci, C. & <u>Camporeale, E</u> <br>
<b>Neutral Vlasov kinetic theory of magnetized plasmas</b>.<br>
<i> Phys. Plasmas., </i> 22, 020704 (2015)<font size="2.5"><a href="https://homepages.cwi.nl/~camporea/papers/1.4907665.pdf" target="_blank"> Download full text</a></font>
<li>Tronci, C., Tassi, E., <u>Camporeale, E.</u>, & Morrison, P. J. (2014). <br>
<b>Hybrid Vlasov-MHD models: Hamiltonian vs. non-Hamiltonian</b><br>
<i>Plasma Phys. and Controlled Fusion </i> 56 095008
<font size="2.5"> <a href="http://arxiv.org/pdf/1403.2773v2" target="_blank"> Download full text </a> </font>
<li>Delzanno, G. L., <u>Camporeale, E.</u>, Moulton, J. D., Borovsky, J. E., MacDonald, E. A., & Thomsen, M. F. (2013)<br>
<b>CPIC: A Curvilinear Particle-in-Cell Code for Plasma--Material Interaction Studies.</b><br>
<i> IEEE Transactions on Plasma Science</i>, 41, 12, 3577
<font size="2.5"> <a href="papers/06675865.pdf" target="_blank"> Download full text </a> </font>
<li>Delzanno, G. L., & <u>Camporeale, E.</u> (2013).<br>
<b>On particle movers in cylindrical geometry for Particle-In-Cell simulations.</b>
<i>J. Comput. Physics</i>, 253, 259-277.
<font size="2.5"> <a href="papers/1-s2.0-S0021999113004798-main.pdf" target="_blank"> Download full text </a> </font>
<li>Markidis, S., <u>Camporeale, E.</u>, Burgess, D., Rizwan-uddin, Lapenta, G. (2009)<br>
<b>Parsek2D: An Implicit Parallel Particle-in-Cell Code</b><br>
<i>Numerical Modeling of Space Plasma Flows: ASTRONUM - 2008 </i> 406, 237
<font size="2.5"> <a href="papers/Markidis_astronum08.pdf" target="_blank"> Download full text </a> </font>
<li><u>Camporeale, E.</u>, G.L. Delzanno, W. Daughton, and G. Lapenta (2006)<br>
<b> New approach for the study of linear Vlasov stability of inhomogeneous systems </b><br>
<i> Phys. Plasmas. </i> 13, 092110
<font size="2.5"> <a href="papers/PhysPlasmas_13_092110-1.pdf" target="_blank"> Download full text </a> </font>
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