We provide the opportunity for the joint Ph.D. program between UCSD and SDSU.
Do you know that measurement in a turbulent environment has its own "sixth
sense"? In fluid dynamical systems, measurements can be used to figure out
events that happened far away from the probing location.
Through state-of-art data assimilation techniques, we can trace back the origin
of any information we have measured. We used this technique to locate the
release of a pollution release, and reconstruct unknown flow fields from limited
Are you self-motivated to do a Ph.D. in interdisciplinary researches about fluid
dynamics, inverse problems, and optimization? We are looking for Ph.D. students
that are willing to spend time studying in an encouraging and creative
There has been a long-hovering question about how to combine experimental
measurements with numerical simulations. Especially in terms of designing a
turbulence model that agrees with experimental studies. In addition, the design
of sensor networks or sensor weighting can be optimized in terms of the amount
of information obtained.
In this Ph.D. project, you will develop novel simulation techniques that combine
machine learning techniques with data assimilation.
The required skills and preferred profile
We are looking for self-motivated young researchers from mechanical
engineering, aerospace engineering, computational physics, applied mathematics,
or other closely related areas.
Familiar with MATLAB and FORTRAN with MPI.
C++ and python is a plus.
Good conceptual understanding of calculus and linear algebra.
Experience with simple machine learning algorithms.
Good communication skills including presentation skills, academic writing
with latex or word.
Please note that the GRE is required for all JDP applicants and cannot be
Having a part-time hobby is a plus.
Work is carried out in the Data Assimilation group at Aerospace Engineering, San Diego State University.
We study various inverse problems in fluid dynamics using numerical simulations
with the discrete adjoint operator.
For further information, please visit us at https://qiwang.sdsu.edu/
Information and application
Interested applicants should visit https://www.engineering.sdsu.edu/admissions/jointdoc_areomech.aspx
for more details about applying for the joint program.
Meanwhile, please reach out to Qi Wang (email@example.com), including:
• A short description of your qualifications and motivation to apply for this
• CV or resume.
• Transcripts from your Bachelor and Master degrees.
Selected candidates will be invited to an interview and should prepare a
scientific presentation as part of the requirements.
We highly value diversity at our university. Applicants from all backgrounds are
标准的限制方法是保证插值后网格顶点（vertice）的值不会出现极大值或极小值（比周围网格的最大值大或比周围网格的最小值小）因为梯度插值最容易出现越界值的就是在顶点处，从而用一个标量来scale梯度gradient；另外一种方法是保证网格的面心midponts of cell faces处不出现极大值或极小值即可，就认为满足了插值后不存在越界的问题。我想的是关于第二种方法，面心处插值后不出现最大值或最小值不能代表顶点处不出现最值，对吗