Merge branch 'main' into yanchenliu_finalproject

_bibliography/2023-11-11-physics_loss.bib

@@ -0,0 +1,49 @@
+@article{1,
+title = {Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows},
+journal = {Proceedings of the Combustion Institute},
+volume = {38},
+number = {2},
+pages = {2617-2625},
+year = {2021},
+author = {Bode, M. and Gauding, M. and Lian, Z. and Denker, D. and Davidovic, M. and Kleinheinz, K. and Jitsev, J. and Pitsch, H.}
+}
+
+@article{2,
+author = {Fukami, K. and Fukagata, K. and Taira, K.},
+title = {Super-resolution recontruction of turbulent flows with machine learning},
+journal = {Journal of Fluid Mechanics},
+year = {2019},
+volume = {870},
+number = {},
+pages = {106-120},
+}
+
+@unpublished{3,
+author = {Fukami, K.,Fukagata, K., & Taira, K.},
+title = {Super-Resolution Analysis Via Machine Learning:
+A Survey For Fluid Flows
+Kai Fukami},
+month = {Jun},
+year = {2023},
+annote = {}
+}
+
+@inproceedings{4,
+author = {Wang, C. and Li, S. and He, D. and Wang, L.},
+title = {Is L2 Physics-Informed Loss Always Suitablefor Training Physics-Informed Neural Network?},
+year = {2022},
+organization = {ConferenceonNeuralInformationProcessingSystems},
+}
+
+@unpublished{5,
+author = {Bischof, R.,Kraus, M.},
+title = {Multi-Objective Loss Balancing for Physics-Informed DeepLearning},
+year = {2022},
+}
+
+
+
+
+
+
+

_config.yml

@@ -338,4 +338,4 @@ mdb:
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 medium_zoom:
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+  integrity: "sha256-EdPgYcPk/IIrw7FYeuJQexva49pVRZNmt3LculEr7zM="

_includes/embed-audio.html

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