Waves and Probability



προς τιμήν του ομότ. καθηγητή ΕΜΠ Γεράσιμου Α. Αθανασούλη

4-5 Ιουλίου 2022, Αμφιθέατρο Πολυμέσων Βιβλιοθήκης ΕΜΠ

Σύμφωνα με τη σχετική ανακοίνωση, η οργανωτική επιτροπή προετοίμασε το πρόγραμμα του επιστημονικού συνεδρίου "Κύματα, Πιθανότητες και Αναμνήσεις" προς τιμήν του ομότιμου καθηγητή ΕΜΠ Γεράσιμου Α. Αθανασούλη.


Στο πρόγραμμα του συνεδρίου περιλαμβάνονται οι εξής κεντρικοί ομιλητές:

Video Recordings

Βιντεοσκοπήσεις των ομιλιών καθώς και τα slides και σχετικές επιστημονικές εργασίες για κάθε παρουσίαση είναι διαθέσιμα στο πρόγραμμα.


Δευτέρα, 4 Ιουλίου

9:00-9:30 Χαιρετισμοί

9:30-10:50 Session 1 Συντονιστής: Γ. Μακράκης (Πανεπιστήμιο Κρήτης)

Διάλειμμα για καφέ

11:00-12:00 Keynote: The Unified Transform and Water Waves    
Αθανάσιος Φωκάς (University of Cambridge)



Διάλειμμα για μεσημεριανό

13:30-14:50 Session 2 Συντονιστής: Κ. Μπελιμπασάκης (ΕΜΠ)

Διάλειμμα για καφέ

15:20-18:00 Session 3 Συντονιστής: Β. Κατσαρδή (Πανεπιστήμιο Θεσσαλίας)

Τριτη, 5 Ιουλιου

9:10-10:50 Session 4 Συντονιστής: Α. Τζαβάρας (KAUST)

Διάλειμμα για καφέ

11:00-12:00 Keynote: Extreme event statistics of motions and loads for ships subjected to random waves      
Θέμης Σαψής (Massachusetts Institute of Technology)

For a number of structural and fatigue problems in marine science, engineers need accurate statistics for kinematic and dynamical quantities, such as displacements and bending moments. For many such problems the response statistics depend nonlinearly on input stochastic processes, such as sea surface elevation. Nonlinearity prevents the use of standard linear methods, leading to the adoption of expensive experiments and time-consuming numerical simulations. In order to avoid this cost we present a machine learning framework to minimize the training set requirements. This framework consists of two parts. First, we use the Karhunen-Loève theorem to represent stochastic sea states with finite-time wave episodes, which have low dimensionality that nonetheless captures the features important to hydrodynamics and structural mechanics. However, the choice of the wave episode duration is ‘caught’ between the Scylla of slow Karhunen- Loève series convergence for long time durations and the Charybdis of missing transient behaviors when the interval is short. To combat this dilemma, we propose a division into a region, designed for parametric interpolation and machine learning, and a stochastic prelude region, designed to probabilistically model transients. The second part of the framework consists of a Gaussian Process Regression (GPR) model designed to learn the mapping from wave episodes to structural outputs. GPR is able to take advantage of the low dimensional parametric representation of the sea state in order to converge with reasonably-sized training sets (on the order n ≈ 100). At the same time, we use a low-dimensional representation in order to represent the stochastic response time series. The principal advantages of the Gaussian process surrogate are the blazing speed of evaluation – ten thousand times faster than the direct method – and the built in uncertainty quantification. Taken together, we can reconstruct the statistics of the responses by sampling sea states via the Karhunen-Loève construction, estimating the corresponding model outputs using the trained GPR, and estimating statistics of interest through Monte-Carlo calculation on the surrogate model.

Dr. Sapsis is Professor of Mechanical and Ocean Engineering at MIT. He is also affiliated with the Institute for Data, Systems and Society (IDSS) and the Center for Computational Science and Engineering (CSSE). He received a Diploma in Naval Architecture and Marine Engineering from Technical University of Athens, Greece and a Ph.D. in Mechanical and Ocean Engineering from MIT. Before his faculty appointment at MIT he served as Research Scientist at the Courant Institute of Mathematical Sciences at New York University. He has also been a visiting faculty at ETH-Zurich. Prof. Sapsis work lies on the interface of nonlinear dynamical systems, probabilistic modeling and data-driven methods. A particular emphasis of his work is the formulation of mathematical methods for the prediction, statistical quantification and optimization of complex engineering and physical systems such as turbulent fluid flows in engineering and geophysical settings, nonlinear ocean waves, and extreme ship motions. He has received numerous awards including three Young Investigator Awards (Navy, Army and Air-Force research office), the Alfred P. Sloan Foundation Award, and more recently the Bodossaki Award on Basic Sciences: Mathematics.

Διάλειμμα για μεσημεριανό

13:25-14:45 Session 5 Συντονιστής: Θ. Γεροστάθης (Πανεπιστήμιο Δυτικής Αττικής)

Διάλειμμα για καφέ

15:00-17:30 Session 6 Συντονιστής: Κ. Σπύρου (ΕΜΠ)

Οργανωτικη Επιτροπη

  • Θ. Γεροστάθης (Πανεπιστήμιο Δυτικής Αττικής)
  • Γ. Μακράκης (Πανεπιστήμιο Κρήτης)
  • Κ. Μαμής (North Carolina State University)
  • Κ. Μπελιμπασάκης (ΕΜΠ)
  • Θ. Σαψής (Massachusetts Institute of Technology)

Web and Multimedia Chair: Μάνος Αθανασούλης (Boston University)