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¹°¸®Áö½Ä±â¹Ý ÀΰøÁö´É Scientific Machine Learning Workshop

 
  ´ëÇѱâ°èÇÐȸ ȸ¿ø ¿©·¯ºÐ, 2023³â 8¿ù¿¡ °³ÃÖµÈ '¹°¸®Áö½Ä±â¹Ý ÀΰøÁö´É' ¿¬±¸ ±³·ùȸ¿¡ À̾î À̹ø 2024³â 1¿ù¿¡´Â Scientific Machine Learning ºÐ¾ß¿¡ ÁýÁßÇÏ¿© ´ëÇпø»ýµéÀÇ ¿¬±¸ ¹ßÇ¥¸¦ Áß½ÉÀ¸·Î ¿¬±¸ ±³·ùȸ¸¦ ÁøÇàÇÕ´Ï´Ù. ÀÌ ¿¬±¸ ±³·ùȸ´Â ÀΰøÁö´É°ú ±â°è µµ¸ÞÀÎ Áö½ÄÀ» À¶ÇÕÇÏ¸ç »õ·Î¿î ¾ÆÀ̵ð¾î¿Í Çõ½ÅÀûÀÎ ¿¬±¸¸¦ ÃËÁøÇÏ´Â ÀÚ¸®°¡ µÉ °ÍÀÔ´Ï´Ù. ƯÈ÷ ´ëÇпø»ýµéÀÇ Àû±ØÀûÀÎ Âü¿©¿Í È°¹ßÇÑ Åä·ÐÀ» ±â´ëÇÏ°í ÀÖ½À´Ï´Ù. »õ·Î¿î ÅëÂû°ú Áö½ÄÀÇ °øÀ¯¸¦ ÅëÇØ Çй®ÀûÀÎ ¼ºÀåÀÇ ±âȸ°¡ µÇ¸®¶ó ¹Ï½À´Ï´Ù. ¸¹Àº Âü¿© ºÎŹµå¸³´Ï´Ù.
 
ÁÖ       ÃÖ : ´ëÇѱâ°èÇÐȸ, Çѱ¹°úÇбâ¼ú¿ø
ÁÖ       °ü : ´ëÇѱâ°èÇÐȸ ±â°èÀΰøÁö´É¿¬±¸È¸
°³ÃÖ ÀÏÀÚ : 2024³â 1¿ù 31ÀÏ(¼ö) ~ 2¿ù 1ÀÏ(¸ñ)
°³ÃÖ Àå¼Ò : Çѱ¹°úÇбâ¼ú¿ø ±â°è°øÇе¿ 1Ãþ ´ëȸÀǽÇ(1601È£)
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1¿ù 31ÀÏ
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13:20 ~ 13:50
Recent Trend in PINN and its Applications to NDT
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(GIST, ±â°è)
13:50 ~ 14:20
PINN for Extreme Mechanics Problems
ÀÌÁ¤¼ö ±³¼ö
(°¡Ãµ´ë, ±â°è)
14:20 ~ 14:40
Multiphysics-informed Neural Networks for Non-Destructive
Structural Health Monitoring in Thermomechanical Systems
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(ÀüºÏ´ë, ±â°è°ú)
14:40 ~ 15:00
Multiphysics-informed Deep Operator Networks for Predicting
the Response of a Permanent Magnet Synchronous Motor
¼Õ¼¼È£ Çлý
(ÇѾç´ë, ±â°è°ú)
15:00 ~ 15:30
Break (´Ù°ú Á¦°ø)
 
15:30 ~ 16:00
Theory-guided Machine Learning Approach
for Singular Perturbation Problems
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(KAIST, ¼öÇÐ)
16:00 ~ 16:30
Sobolev Training for Neural Networks and its Applications
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(Çѹç´ë, ÀΰøÁö´É)
16:30 ~ 16:50
Application of PINNs to Argon Glow Discharge Models
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(Æ÷Ç×°ø´ë, ¼öÇаú)
16:50 ~
18:00
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2¿ù 1ÀÏ
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10:00 ~ 10:20
Physics-informed Fourier Representation
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(Æ÷Ç×°ø´ë, ±â°è°ú)
10:20 ~ 10:40
Prediction of Thermal Runaway for a Lithium-ion Battery
through Multiphysics-informed DeepONet
Á¤ÁøÈ£ Çлý
(ÇѾç´ë, ±â°è°ú)
10:40 ~ 11:00
Solving Forward and Inverse Problems of
Cl2 Global Discharge Models using PINNs
±ÇÈñÀç Çлý
(Æ÷Ç×°ø´ë, ¼öÇаú)
11:00 ~ 11:20
Data-driven Discovery of Drag-inducing Elements on
Rough Surfaces through Convolutional Neural Networks
½ÅÈñ¼ö Çлý
(ÀÎÇÏ´ë, ±â°è°ú)
11:20 ~ 11:40
A Full-Field Estimation of Dynamics System Responses
with Sparse Measurement
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(ÀüºÏ´ë, ±â°è°ú)
11:40 ~
12:00
Solving Boltzmann-BGK Equation with
Physics-informed Neural Networks
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(Ä«À̽ºÆ®, ¼öÇаú)
 
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  • ½Åû ¹æ¹ý : ´ëÇѱâ°èÇÐȸ ȨÆäÀÌÁö [»çÀüµî·Ï ¹Ù·Î°¡±â]
  • ½Åû ¸¶°¨ : 2024³â 1¿ù 24ÀÏ(¼ö)±îÁö(´Ü, ³³ºÎ ±âÁØ ¼±Âø¼ø 60¸í, ÇöÀåµî·Ï ºÒ°¡) → Á¢¼ö ¸¶°¨
  • °áÁ¦ ¹æ¹ý 
  - ¹«ÅëÀå ÀÔ±Ý : ¿ì¸®ÀºÇà / ´ëÇѱâ°èÇÐȸ / 1005-403-359047
  - Ä«µå°áÁ¦ ¹× °èÁÂÀÌü : »çÀüµî·Ï ÆäÀÌÁö¿¡¼­ ÀüÀÚ°áÁ¦ ½Ã½ºÅÛ ÀÌ¿ë
 
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Çà»ç ¹®ÀÇ : À̽Âö ±³¼ö (seunglee@kaist.ac.kr) 
 
 
 

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