diff --git a/references.bib b/references.bib
index e082cf3..7ec484c 100644
--- a/references.bib
+++ b/references.bib
@@ -1,5 +1,43 @@
 
 
+@article{bruna_neural_2024,
+	title = {Neural {Galerkin} schemes with active learning for high-dimensional evolution equations},
+	volume = {496},
+	issn = {0021-9991},
+	doi = {10.1016/j.jcp.2023.112588},
+	journal = {Journal of Computational Physics},
+	author = {Bruna, J. and Peherstorfer, B. and Vanden-Eijnden, E.},
+	year = {2024},
+	note = {Publisher: Elsevier BV},
+	pages = {112588},
+}
+
+@article{lu_learning_2021,
+	title = {Learning nonlinear operators via {DeepONet} based on the universal approximation theorem of operators},
+	volume = {3},
+	issn = {2522-5839},
+	doi = {10.1038/s42256-021-00302-5},
+	number = {3},
+	journal = {Nat. Mach. Intell.},
+	author = {Lu, L. and Jin, P. and Pang, G. and Zhang, Z. and Karniadakis, G. E.},
+	year = {2021},
+	note = {Publisher: Springer Science and Business Media LLC},
+	pages = {218--229},
+}
+
+@article{karniadakis_physics-informed_2021,
+	title = {Physics-informed machine learning},
+	volume = {3},
+	issn = {2522-5820},
+	doi = {10.1038/s42254-021-00314-5},
+	number = {6},
+	journal = {Nat. Rev. Phys.},
+	author = {Karniadakis, G. E. and Kevrekidis, I. G. and Lu, L. and Perdikaris, P. and Wang, S. and Yang, L.},
+	year = {2021},
+	note = {Publisher: Springer Science and Business Media LLC},
+	pages = {422--440},
+}
+
 @misc{saigre_mesh_2024,
 	title = {Mesh and configuration files to perform coupled heat+fluid simulations on a realistic human eyeball geometry with {Feel}++},
 	copyright = {Creative Commons Attribution 4.0 International},
@@ -666,30 +704,3 @@ @inproceedings{gamblin_spack_2015
 	year = {2015},
 	pages = {1--12},
 }
-
-@article{vallet_toward_2022,
-	title = {Toward practical transparent verifiable and long-term reproducible research using {Guix}},
-	volume = {9},
-	issn = {2052-4463},
-	url = {https://www.nature.com/articles/s41597-022-01720-9},
-	doi = {10.1038/s41597-022-01720-9},
-	abstract = {Abstract
-            Reproducibility crisis urge scientists to promote transparency which allows peers to draw same conclusions after performing identical steps from hypothesis to results. Growing resources are developed to open the access to methods, data and source codes. Still, the computational environment, an interface between data and source code running analyses, is not addressed. Environments are usually described with software and library names associated with version labels or provided as an opaque container image. This is not enough to describe the complexity of the dependencies on which they rely to operate on. We describe this issue and illustrate how open tools like Guix can be used by any scientist to share their environment and allow peers to reproduce it. Some steps of research might not be fully reproducible, but at least, transparency for computation is technically addressable. These tools should be considered by scientists willing to promote transparency and open science.},
-	language = {en},
-	number = {1},
-	urldate = {2024-09-05},
-	journal = {Scientific Data},
-	author = {Vallet, Nicolas and Michonneau, David and Tournier, Simon},
-	month = oct,
-	year = {2022},
-	pages = {597},
-}
-
-@techreport{adams_dakota_2022,
-	title = {Dakota, {A} {Multilevel} {Parallel} {Object}-{Oriented} {Framework} for {Design} {Optimization}, {Parameter} {Estimation}, {Uncertainty} {Quantification}, and {Sensitivity} {Analysis}: {Version} 6.16 {User}’s {Manual}},
-	number = {SAND2022-6171},
-	institution = {Sandia National Laboratories},
-	author = {Adams, B. M. and Bohnhoff, W. J. and Dalbey, K. R. and Ebeida, M. S. and Eddy, J. P. and Eldred, M. S. and Hooper, R. W. and Hough, P. D. and Hu, K. T. and Jakeman, J. D. and Khalil, M. and Maupin, K. A. and Monschke, J. A. and Ridgway, E. M. and Rushdi, A. A. and Seidl, D. T. and Stephens, J. A. and Swiler, L. P. and Winokur, J. G.},
-	month = may,
-	year = {2022},
-}