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Xiao Lab
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The lab reproduces flow phenomena on computers using new computational methods.
AboutAbout the lab
Reproducing complex flows on computers
The Xiao Lab studies a wide range of fluid phenomena through numerical simulation, mainly in mechanical and aerospace engineering.
The lab develops new computational methods and numerical models for solving, on computers, the equations that describe fluid motion.
The lab works broadly, from basic research on computational methods to the integration of data and numerical models, the use of hardware, and applications to real-world problems.
Key terms
- Numerical simulation
- A method of reproducing and predicting the behavior of a phenomenon by solving the equations that describe it on a computer
- Multiphase flow
- A phenomenon in which substances with different properties, such as water and air, flow together while maintaining interfaces between them
- Data assimilation
- A method of using data obtained from observations or experiments to correct errors and uncertainty in a numerical model
NewsLab news
View More ↗ResearchResearch themes
The lab develops new computational methods and numerical models that can faithfully reproduce complex fluid phenomena. It also works on evaluating errors and uncertainty, analysis combined with data and machine learning, and large-scale computation using GPUs.
*Illustrative imageComputational Methods for Shock Waves and Multiphase Flows
The lab develops high-performance numerical schemes and numerical models for computing complex flows, such as shock waves and multiphase flows with moving interfaces, like bubbles and droplets.
*Illustrative imageQuantifying Computational Errors and Uncertainty
The lab quantitatively evaluates how errors and uncertainty contained in a numerical model propagate and change in time and space during a simulation.
*Illustrative imageIntegrating Data and Numerical Models
Using data assimilation, which uses observational and experimental data, the lab identifies and corrects uncertainty in numerical models. It is building fluid analysis systems that integrate experiments and numerical models.
*Illustrative imageNumerical Models Using Machine Learning
The lab develops numerical analysis models that incorporate machine learning and AI technologies such as neural networks. A key challenge is devising learning methods that can also make use of known physical laws.
MembersFaculty
The members of the Xiao Lab consist of faculty, administrative staff, visiting researchers, and doctoral, master's, and undergraduate students.As of 2026
- ProfessorXiao FengPublications ↗
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Rayleigh-Taylor instability” Lawrence Livermore National Laboratory / Public domain (cropped)
- Computational Methods for Shock Waves and Multiphase Flows: “10deg-Mach3-cone-PSUSWT-color-schlieren” Settles1 / CC BY-SA 4.0 (cropped)
- Quantifying Computational Errors and Uncertainty: “Helicopter blade tip vortex simulation by DLR” DLR / CC BY 3.0 (cropped)
- Integrating Data and Numerical Models: “Lamb-Oseen vortex” Ddcampayo / CC BY-SA 3.0 (cropped)
- Numerical Models Using Machine Learning: “UVLM simulation of an aircraft model” GuillermoHazebrouck / CC BY-SA 4.0 (cropped)
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- Related lab (西迫研究室): “T Junction Microfluidic Device” Wongbria / CC BY-SA 4.0 (cropped)
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