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Writing/test.bib", "201. Metadata/My Library/files/4011/contextual.css", diff --git a/1000s Templates/Literature Note.md b/1000s Templates/Literature Note.md index 951762496..017151e3c 100755 --- a/1000s Templates/Literature Note.md +++ b/1000s Templates/Literature Note.md @@ -11,7 +11,7 @@ authors: {% endfor %} citekey: "{{ citekey }}" {% if itemType == "journalArticle" %} -journal: "_{{ publicationTitle }}_" +journal: "{{ publicationTitle }}" {% endif %} {% if volume %} volume: {{ volume }} @@ -51,9 +51,6 @@ pages: {{ pages }} | replace(']', '') }}{% if not loop.last %}, {% endif %}{% endfor %} -## Zotero Link -[Attachment] - >[!Abstract] >{% if abstractNote %}{{abstractNote}}{% endif %} @@ -63,14 +60,14 @@ pages: {{ pages }} # Annotations {% macro calloutHeader(type, color) -%} -{%- if type == "highlight" -%} +{% if type == "highlight" %} Quote -{%- endif -%} +{% endif %} -{%- if type == "text" -%} +{% if type == "text" %} Note -{%- endif -%} -{%- endmacro -%} +{% endif %} +{% endmacro %} {% persist "annotations" %} {% set newAnnotations = annotations | filterby("date", "dateafter", lastImportDate) %} diff --git a/200. Library Papers/My Library.bib b/200. Library Papers/My Library.bib index 0d8f60c70..0ab40fdb8 100644 --- a/200. Library Papers/My Library.bib +++ b/200. Library Papers/My Library.bib @@ -6772,6 +6772,7 @@ for defect classification of TFT–LCD panels.pdf} urldate = {2024-05-20}, abstract = {Abstract Lagrangian turbulence lies at the core of numerous applied and fundamental problems related to the physics of dispersion and mixing in engineering, biofluids, the atmosphere, oceans and astrophysics. Despite exceptional theoretical, numerical and experimental efforts conducted over the past 30 years, no existing models are capable of faithfully reproducing statistical and topological properties exhibited by particle trajectories in turbulence. We propose a machine learning approach, based on a state-of-the-art diffusion model, to generate single-particle trajectories in three-dimensional turbulence at high Reynolds numbers, thereby bypassing the need for direct numerical simulations or experiments to obtain reliable Lagrangian data. Our model demonstrates the ability to reproduce most statistical benchmarks across time scales, including the fat-tail distribution for velocity increments, the anomalous power law and the increased intermittency around the dissipative scale. Slight deviations are observed below the dissipative scale, particularly in the acceleration and flatness statistics. Surprisingly, the model exhibits strong generalizability for extreme events, producing events of higher intensity and rarity that still match the realistic statistics. This paves the way for producing synthetic high-quality datasets for pretraining various downstream applications of Lagrangian turbulence.}, langid = {english}, + keywords = {Diffusion,Turbulence}, file = {/home/danesabo/Zotero/storage/A6FPHV6L/Li et al. - 2024 - Synthetic Lagrangian turbulence by generative diff.pdf} } diff --git a/200. Library Papers/liSyntheticLagrangianTurbulence2024.md b/200. Library Papers/liSyntheticLagrangianTurbulence2024.md new file mode 100644 index 000000000..088560fa2 --- /dev/null +++ b/200. Library Papers/liSyntheticLagrangianTurbulence2024.md @@ -0,0 +1,61 @@ +--- +readstatus: false +dateread: +title: "Synthetic Lagrangian turbulence by generative diffusion models" +year: 2024 +authors: + + + - "Li, T." + + - "Biferale, L." + + - "Bonaccorso, F." + + - "Scarpolini, M. A." + + - "Buzzicotti, M." + + +citekey: "liSyntheticLagrangianTurbulence2024" + +journal: "Nature Machine Intelligence" + + +volume: 6 + + +issue: 4 + + + + + +pages: 393-403 + +--- +# Indexing Information +## DOI +[10.1038/s42256-024-00810-0](https://doi.org/10.1038/s42256-024-00810-0) +## ISBN +[](https://www.isbnsearch.org/isbn/) +## Tags: +#Diffusion, #Turbulence + +>[!Abstract] +>Abstract + Lagrangian turbulence lies at the core of numerous applied and fundamental problems related to the physics of dispersion and mixing in engineering, biofluids, the atmosphere, oceans and astrophysics. Despite exceptional theoretical, numerical and experimental efforts conducted over the past 30 years, no existing models are capable of faithfully reproducing statistical and topological properties exhibited by particle trajectories in turbulence. We propose a machine learning approach, based on a state-of-the-art diffusion model, to generate single-particle trajectories in three-dimensional turbulence at high Reynolds numbers, thereby bypassing the need for direct numerical simulations or experiments to obtain reliable Lagrangian data. Our model demonstrates the ability to reproduce most statistical benchmarks across time scales, including the fat-tail distribution for velocity increments, the anomalous power law and the increased intermittency around the dissipative scale. Slight deviations are observed below the dissipative scale, particularly in the acceleration and flatness statistics. Surprisingly, the model exhibits strong generalizability for extreme events, producing events of higher intensity and rarity that still match the realistic statistics. This paves the way for producing synthetic high-quality datasets for pretraining various downstream applications of Lagrangian turbulence. + +>[!note] Markdown Notes +>None! + + +# Annotations + + +%% begin annotations %% + + +%% end annotations %% + +%% Import Date: 2024-09-04T14:46:34.638-04:00 %% diff --git a/201. Metadata/My Library.bib b/201. Metadata/My Library.bib index 0d8f60c70..0ab40fdb8 100644 --- a/201. Metadata/My Library.bib +++ b/201. Metadata/My Library.bib @@ -6772,6 +6772,7 @@ for defect classification of TFT–LCD panels.pdf} urldate = {2024-05-20}, abstract = {Abstract Lagrangian turbulence lies at the core of numerous applied and fundamental problems related to the physics of dispersion and mixing in engineering, biofluids, the atmosphere, oceans and astrophysics. Despite exceptional theoretical, numerical and experimental efforts conducted over the past 30 years, no existing models are capable of faithfully reproducing statistical and topological properties exhibited by particle trajectories in turbulence. We propose a machine learning approach, based on a state-of-the-art diffusion model, to generate single-particle trajectories in three-dimensional turbulence at high Reynolds numbers, thereby bypassing the need for direct numerical simulations or experiments to obtain reliable Lagrangian data. Our model demonstrates the ability to reproduce most statistical benchmarks across time scales, including the fat-tail distribution for velocity increments, the anomalous power law and the increased intermittency around the dissipative scale. Slight deviations are observed below the dissipative scale, particularly in the acceleration and flatness statistics. Surprisingly, the model exhibits strong generalizability for extreme events, producing events of higher intensity and rarity that still match the realistic statistics. This paves the way for producing synthetic high-quality datasets for pretraining various downstream applications of Lagrangian turbulence.}, langid = {english}, + keywords = {Diffusion,Turbulence}, file = {/home/danesabo/Zotero/storage/A6FPHV6L/Li et al. - 2024 - Synthetic Lagrangian turbulence by generative diff.pdf} } diff --git a/[Full.md b/[Full.md new file mode 100644 index 000000000..e69de29bb diff --git a/{uri}.md b/{uri}.md new file mode 100644 index 000000000..e69de29bb