Περιεχόμενο Μαθήματος
IEEE Computational Science and Engineering, Summer 1994).
1. PSEs for Computational Science and Engineering
2. The Software Architecture of PSEs
3. Review of MatLab, Python, Sage PSEs
4. Comparison of PSEs (student presentation)
5. Short overview of the functionality of Matlab (student presentation)
6. Short overview of the functionality of Python (student presentation)
7. Short overview of the functionality of Sage (student presentation)
8. Application of MatLab, Python, Sage PSEs (student projects)
a) Numerical Analysis Problems
b) Optimization
c) Robotics
d) Control
e) Fuzzy logic and neural net computing
f) AI and data mining
g) Dynamics
h) Signal processing
i) Financial Engineering
9. Parallel and Network PSEs
10. References
a) Enabling Technologies of PSEs (E.N. Houstis et. al. eds)
b) The Role of Solving Environments in Engineering and Mathematics Education (E.N. Houstis)
c) Educating the Engineer of 2020: Adapting Engineering Education to the 21st Century (National Academy of Engineering)
d) Εγχειρίδιο Αριθμητικών Μεθόδων με Επιστημονικές Εφαρμογές (Η. Χούστης)
e) Bibliography for MatLab, Python, and Sage
' rows='20' cols='70'>
Problem Solving Environments (PSEs)
A PSE is a computer system that provides all the computational facilities needed to solve a target class of problems. These features include advanced solution methods, automatic and semiautomatic selection of solution methods, and ways to easily incorporate novel solution methods. Moreover, PSEs use the language of the target class of problems, so users can run them without specialized knowledge of the underlying computer hardware or software. By exploiting modern technologies such as interactive color graphics, powerful processors, and networks of specialized services, PSEs can track extended problem solving tasks and allow users to review them easily. Overall, they create a framework that is all things to all people: they solve simple or complex problems, support rapid prototyping or detailed analysis, and can be used in introductory education or at the frontiers of science.
From Computer as Thinker/Doer: Problem-Solving Environments for Computational Science by Stratis Gallopoulos, Elias Houstis and John Rice (IEEE Computational Science and Engineering, Summer 1994).
1. PSEs for Computational Science and Engineering
2. The Software Architecture of PSEs
3. Review of MatLab, Python, Sage PSEs
4. Comparison of PSEs (student presentation)
5. Short overview of the functionality of Matlab (student presentation)
6. Short overview of the functionality of Python (student presentation)
7. Short overview of the functionality of Sage (student presentation)
8. Application of MatLab, Python, Sage PSEs (student projects)
a) Numerical Analysis Problems
b) Optimization
c) Robotics
d) Control
e) Fuzzy logic
f) AI and data mining
g) Dynamics
h) Signal processing
i) Financial Engineering
9. Parallel and Network PSEs
10. References
a) Enabling Technologies of PSEs (E.N. Houstis et. al. eds)
b) The Role of Solving Environments in Engineering and Mathematics Education (E.N. Houstis)
c) Educating the Engineer of 2020: Adapting Engineering Education to the 21st Century (National Academy of Engineering)
d) Εγχειρίδιο Αριθμητικών Μεθόδων με Επιστημονικές Εφαρμογές (Η. Χούστης)
e) Bibliography for MatLab, Python, and Sage
' rows='20' cols='70'>A PSE is a computer system that provides all the computational facilities needed to solve a target class of problems. These features include advanced solution methods, automatic and semiautomatic selection of solution methods, and ways to easily incorporate novel solution methods. Moreover, PSEs use the language of the target class of problems, so users can run them without specialized knowledge of the underlying computer hardware or software. By exploiting modern technologies such as interactive color graphics, powerful processors, and networks of specialized services, PSEs can track extended problem solving tasks and allow users to review them easily. Overall, they create a framework that is all things to all people: they solve simple or complex problems, support rapid prototyping or detailed analysis, and can be used in introductory education or at the frontiers of science.
From Computer as Thinker/Doer: Problem-Solving Environments for Computational Science by Stratis Gallopoulos, Elias Houstis and John Rice (IEEE Computational Science and Engineering, Summer 1994).
1. PSEs for Computational Science and Engineering
2. The Software Architecture of PSEs
3. Review of MatLab, Python, Sage PSEs
4. Comparison of PSEs (student presentation)
5. Short overview of the functionality of Matlab (student presentation)
6. Short overview of the functionality of Python (student presentation)
7. Short overview of the functionality of Sage (student presentation)
8. Application of MatLab, Python, Sage PSEs (student projects)
a) Numerical Analysis Problems
b) Optimization
c) Robotics
d) Control
e) Fuzzy logic and neural net computing
f) AI and data mining
g) Dynamics
h) Signal processing
i) Financial Engineering
9. Parallel and Network PSEs
10. References
a) Enabling Technologies of PSEs (E.N. Houstis et. al. eds)
b) The Role of Solving Environments in Engineering and Mathematics Education (E.N. Houstis)
c) Educating the Engineer of 2020: Adapting Engineering Education to the 21st Century (National Academy of Engineering)
d) Εγχειρίδιο Αριθμητικών Μεθόδων με Επιστημονικές Εφαρμογές (Η. Χούστης)
e) Bibliography for MatLab, Python, and Sage
' rows='20' cols='70'>
Problem Solving Environments (PSEs)
A PSE is a computer system that provides all the computational facilities needed to solve a target class of problems. These features include advanced solution methods, automatic and semiautomatic selection of solution methods, and ways to easily incorporate novel solution methods. Moreover, PSEs use the language of the target class of problems, so users can run them without specialized knowledge of the underlying computer hardware or software. By exploiting modern technologies such as interactive color graphics, powerful processors, and networks of specialized services, PSEs can track extended problem solving tasks and allow users to review them easily. Overall, they create a framework that is all things to all people: they solve simple or complex problems, support rapid prototyping or detailed analysis, and can be used in introductory education or at the frontiers of science.
From Computer as Thinker/Doer: Problem-Solving Environments for Computational Science by Stratis Gallopoulos, Elias Houstis and John Rice (IEEE Computational Science and Engineering, Summer 1994).
1. PSEs for Computational Science and Engineering
2. The Software Architecture of PSEs
3. Review of MatLab, Python, Sage PSEs
4. Comparison of PSEs (student presentation)
5. Short overview of the functionality of Matlab (student presentation)
6. Short overview of the functionality of Python (student presentation)
7. Short overview of the functionality of Sage (student presentation)
8. Application of MatLab, Python, Sage PSEs (student projects)
a) Numerical Analysis Problems
b) Optimization
c) Robotics
d) Control
e) Fuzzy logic
f) AI and data mining
g) Dynamics
h) Signal processing
i) Financial Engineering
9. Parallel and Network PSEs
10. References
a) Enabling Technologies of PSEs (E.N. Houstis et. al. eds)
b) The Role of Solving Environments in Engineering and Mathematics Education (E.N. Houstis)
c) Educating the Engineer of 2020: Adapting Engineering Education to the 21st Century (National Academy of Engineering)
d) Εγχειρίδιο Αριθμητικών Μεθόδων με Επιστημονικές Εφαρμογές (Η. Χούστης)
e) Bibliography for MatLab, Python, and Sage
' rows='20' cols='70'>From Computer as Thinker/Doer: Problem-Solving Environments for Computational Science by Stratis Gallopoulos, Elias Houstis and John Rice (IEEE Computational Science and Engineering, Summer 1994).
1. PSEs for Computational Science and Engineering
2. The Software Architecture of PSEs
3. Review of MatLab, Python, Sage PSEs
4. Comparison of PSEs (student presentation)
5. Short overview of the functionality of Matlab (student presentation)
6. Short overview of the functionality of Python (student presentation)
7. Short overview of the functionality of Sage (student presentation)
8. Application of MatLab, Python, Sage PSEs (student projects)
a) Numerical Analysis Problems
b) Optimization
c) Robotics
d) Control
e) Fuzzy logic and neural net computing
f) AI and data mining
g) Dynamics
h) Signal processing
i) Financial Engineering
9. Parallel and Network PSEs
10. References
a) Enabling Technologies of PSEs (E.N. Houstis et. al. eds)
b) The Role of Solving Enviro
