serial: '643'
sid: '13621'
uuid: 46310ca8-a73a-43c4-8218-335bf32e6e17
uri: /urop-research-mentor-project-submission-portal
created: '1713582820'
completed: '1713582820'
changed: '1713582820'
in_draft: '0'
current_page: ''
remote_addr: 68.46.228.144
uid: '0'
langcode: en
webform_id: urop_project_proposal_portal
entity_type: node
entity_id: '1116'
locked: '0'
sticky: '0'
notes: ''
data:
  approximately_how_many_hours_a_week_would_the_research_assistant: '10 hours'
  are_you_currently_looking_for_students_: 'Yes'
  confirmation_1: '1'
  contact_email_fsu_email: ''
  contact_email_fsu_email2: ''
  contact_email_fsu_email_if_affiliated_: sussman@math.fsu.edu
  faculty_advisor_confirmation: ''
  faculty_advisor_name: ''
  faculty_advisor_s_fsu_email: ''
  fsu_college: 'Arts and Sciences'
  fsu_department_if_applicable_: mathematics
  headshot_optional_: ''
  if_the_project_location_is_off_campus_does_the_student_need_to_p: ''
  mentoring_philosophy: 'We should meet about once per week in which we go over outstanding problems and questions.  This is "directed independent research."  So it is up to the student to seek out resources (e.g. existing code that is available) that will help in answering the research question.'
  mentor_handbook_and_faqs: '1'
  name_of_other_faculty_collaborator_if_applicable_: ''
  number_of_assistants_needed_faculty_postdoc_max_6_graduate_stude: '2'
  other_faculty_collaborator_s_preferred_pronouns: ''
  overall_research_project_description: 'There have recently been many new developments in solving differential equations using data science techniques.  See for example the "PINN" ("Physics Informed Neural Networks") method developed by Prof. Karniadakis at Brown University.  Are these methods better than classical methods (finite element, finite difference, finite volume, or Runge Kutta methods) for solving differential equations?  It could be that the new ``data science motivated'''' methods are a transformative development since the methods are "embarrassingly vectorizable" (for GPU processing) and have anecdotally been shown to represent solutions using a minimum number of degrees of freedom.  There are many test problems to try  and compare classical methods to data science techniques, all ranging in level of difficulty.  '
  please_add_any_additional_information_here: ''
  please_provide_a_link_to_your_publications_a_video_clip_or_a_web: ''
  please_select_the_choice_that_most_accurately_describes_your_exp: In-person
  please_select_the_location_of_your_project_: 'On FSU Main Campus'
  position_availability_for_student_research: 'Flexible schedule'
  position_title: Faculty
  primary_research_mentor_name: 'Mark Sussman'
  project_keywords: 'differential equations, data science, numerical analysis'
  relevant_student_major_s_: 'open to all majors'
  research_mentor_preferred_pronoun2: ''
  research_mentor_pronouns: ''
  research_mentor_supervisor_if_different_from_above_: ''
  research_tasks_for_student_research_assistant_s_: |-
    Research tasks:
    1. develop a classical numerical method (your choice) for solving an ODE or PDE.  Computer language is flexible.
    2. develop a data science numerical method for solving the same ODE or PDE as in step 1.
    3. Compare (1) to (2) in terms of (i) ease of implementation, (ii) accuracy, and (iii) efficiency.
  roundtable_times_and_zoom_links: ''
  skills_that_research_assistants_may_need_: |-
    required:
    basic programming skills.  Calculus II or higher.
  title_of_the_project: 'Comparing classical methods for solving partial differential equations to machine learning methods.'
  update_url: 'https://cre.fsu.edu/urop-research-mentor-project-submission-portal?token=RSqXh2VL0AhJPXoVdJt_ksM3V0OC0ULP8aPczHmQzD0'
  urop_performance_evaluation: '1'
  urop_poster_presentation: '1'
  when_potential_research_assistants_are_reaching_out_via_email_2: ''
  when_potential_research_assistants_are_reaching_out_via_email_wh: ''
  when_students_are_reaching_out_via_email_what_is_your_preferreda: ''
  would_you_like_to_participate_in_the_urop_research_mentor_roundt: ''
  year: '2024'