Add multi-fidelity accelerator papers

Libensemble · Oct 17, 2023 · 3d246fd · 3d246fd
1 parent d57503f
commit 3d246fd
Show file tree

Hide file tree

Showing 2 changed files with 35 additions and 3 deletions.
diff --git a/docs/papers/joss/paper.bib b/docs/papers/joss/paper.bib
@@ -78,3 +78,35 @@ @techreport{ParMOODesign23
   number = {2304.06881}
 }
 
+@article{PhysRevAccelBeams.26.084601,
+  title = {Bayesian optimization of laser-plasma accelerators assisted by reduced physical models},
+  author = {Ferran Pousa, A. and Jalas, S. and Kirchen, M. and Martinez de la Ossa, A. and Th\'evenet, M. and Hudson, S. and Larson, J. and Huebl, A. and Vay, J.-L. and Lehe, R.},
+  journal = {Phys. Rev. Accel. Beams},
+  volume = {26},
+  issue = {8},
+  pages = {084601},
+  numpages = {9},
+  year = {2023},
+  month = {Aug},
+  publisher = {American Physical Society},
+  doi = {10.1103/PhysRevAccelBeams.26.084601},
+  url = {https://link.aps.org/doi/10.1103/PhysRevAccelBeams.26.084601}
+}
+
+@article{Pousa22,
+year = {2022},
+    author = {Ferran Pousa, A and Jalas, S. and Kirchen, M. and Martinez de la Ossa, A. and Thévenet, M. and Hudson, S. and Larson, J.  and Huebl, A. and Vay, J.-L. and Lehe, R.},
+    title = {Multitask Optimization of Laser-Plasma Accelerators Using Simulation Codes with Different Fidelities},
+    pages = {1761-1764},
+    paper = {WEPOST030},
+    doi = {10.18429/JACoW-IPAC2022-WEPOST030},
+    venue = {Bangkok, Thailand, Jun. 2022},
+      journal = {Proceedings of the 13th International Particle Accelerator Conference},
+    intype = {presented at the},
+    series = {International Particle Accelerator Conference},
+    number = {13},
+    publisher = {JACoW Publishing, Geneva, Switzerland},
+    note = {presented at IPAC'22, Bangkok, Thailand, Jun. 2022},
+    url = {https://accelconf.web.cern.ch/ipac2022/papers/wepost030.pdf},
+    language = {english}
+}
diff --git a/docs/papers/joss/paper.md b/docs/papers/joss/paper.md
@@ -51,7 +51,7 @@ efficient numerical tools, many of which require the same base code (e.g., for
 performing numerical orbit integration). -->
 
 As the number of available computational resources increases, almost all applications
-or evaluations evantually stop perfectly scaling. Nonetheless, clusters, servers, and other resources
+or evaluations eventually stop scaling. Nonetheless, clusters, servers, and other resources
 keep growing, alongside the request to efficiently apply that hardware.
 libEnsemble is a complete Python toolkit and workflow system for intelligently driving
 "ensembles" of experiments or simulations at massive scales. Via a generator-simulator
@@ -62,8 +62,8 @@ design, decision, and inference studies on or across laptops and heterogeneous h
 
 Examples of way in which libEnsemble has been used in science and engineering problems include
 - optimization of variational algorithms on quantum computers [@Liu2022layer]
-- parallelize the ParMOO solver for multiobjective simulation optimization problems [@ParMOODesign23]
-- design of particle accelerators [@Neveu2023]
+- parallelization of the ParMOO solver for multiobjective simulation optimization problems [@ParMOODesign23]
+- design of particle accelerators [@Neveu2023] [@PhysRevAccelBeams.26.084601] [@Pousa22]
 - sequential Bayesian experimental design [@Surer2023] and Bayesian calibration [@MCMPSW2022]
 
 Additional details on the parallel features and scalability of libEnsemble can be found in Refs [@Hudson2022] and [@libensemble-man].