| メール | wagner.takeshi.fn@u.tsukuba.ac.jp |
|---|
| 名前 | WAGNER ALEXANDER TAKESHI |
|---|
| 所属（教員組織）※学生は指導教員の所属を選択 /Affiliation (Faculty/Organization) | 計算科学研究センター/Center for Computational Sciences |
|---|
| 専門分野/ Research Field | Astrophysics |
|---|
| 以下の項目から選んでください。 Please select from the options below. | 教員・研究員/ Faculty Member / Researcher |
|---|
| 職位 Position | Assistant Professor |
|---|
| 2－1研究へのAIの活用経験と意識（当てはまるものを選んでください）/Experience with and Perceptions of AI Utilization in Research | AIを活用して研究を実施したことがある/I have conducted research using AI. |
|---|
| 3-1 AI を活用することで推進したい（推進した）研究テーマを回答ください。（1テーマ40字程度）Please describe the research theme(s) you would like to promote (or have promoted) by utilizing AI. | Accelerating and improving parts of numerical schemes, in particular schemes for compressible magnetohydrodyanmics. |
|---|
| 3-2 AIを活用することで解決したい（解決した）学術的課題の概要を教えてください（分野外の専門家がわかるように。1テーマ100〜300字程度）Please provide an overview of the academic challenge(s) you would like to address (or have addressed) by utilizing AI. | Acceleration of hydrodynamic schemes: Certain routines within hydrodynamical schemes can be bottlenecks within an integration timestep. I have started investigating the possibility of constructing neural networks that can quickly solve Riemann problems (the evolution of a flow across a boundary of two zones) and piecewise polynomial reconstructions (fits to zone average values).
Adaptive mesh refinement: There is a chicken-and-egg problem associated with triggering the refinement (subdividing zones for higher resolution) of parts of a mesh to resolve certain physical processes, e.g., gas cooling: Refinement is desired on rapidly cooling and contracting regions, but a cooling instability may only occur when refined. I have started considering the possibility of creating a neural network that can predict where cooling may likely occur and refine in advance. |
|---|
| 3-3 以下内容がわかる場合は具体的に教えてください。１．研究テーマで AI が特に有効または改善ができる部分はどこ（何）でしょうか？２．AIを活用することによって、研究分野にどのようなインパクトをあたえられるでしょうか？If possible, please provide specific details on the following points: 1Which part(s) ... | 1. Applications of computational fluid dynamics in astrophysics frequently require ad-hoc or artificially sub-grid prescriptions for phenomena that cannot be spatially or temporally resolved. Examples include supernova explosions or the formation of individual stars in simulations that evolve a large fraction of the universe containing tens of thousands of galaxies. Neural networks trained with output from high-resolution simulations of individual galaxies could provide an alternative, more physically motivated subgrid implementation.
2. These sub-grid methods strongly determine the prediction for galaxy statistics, e.g., the correlations between of black hole mass and various galaxy properties. So far, the sub-grid models that parametrize unresolved phenomena have simply been tuned to match predictions. Using physically motivated neural-networks as sub-grid models would provide predictions that are actually testable with observations. If predictions match, this would provide confirmation that both single-galaxy and cosmological scales were correctly linked through their respective simulations. |
|---|
| 3-4 現時点で AI for Science チャレンジ型に応募したいと思いますか？At this point, would you like to apply for the AI for Science Challenge–type program? | わからない not sure. |
|---|
| 3-5 ご自身の研究活動にAIを導入・活用するときの課題があれば教えてください。支援構築の参考にします。（複数選択可）If you have any challenges or concerns regarding the introduction or use of AI in your own research activities, please let us know. | データ整理・前処理が大変そう Data organization and preprocessing seem difficult and time-consuming.; |
|---|
| 3-6 上記テーマのためのデータは既に取得済みですか？ Have the data for the above research theme already been collected? | 現在データはなく、これからデータを取得する予定（前向き研究）The data have not yet been collected, and we plan to collect them in the future (prospective research). |
|---|