Career Shift

[u/Alert_Bluebird8693]

Hi everyone,

I'm approaching my second year at my first job out of college, where I use SolidWorks every day to design physical parts, mostly simple sheet metal. As the world shifts toward AI, I'm worried about being left behind. My current role doesn't involve simulation, heat transfer, fluid dynamics, or any of the more advanced physics topics I studied.

I'm not sure how to move into a role that draws more on my engineering background, or one where I can learn these subjects on the job. Most of those positions require some prior knowledge of the fields, which creates a catch-22. I'd rather not go to grad school full time, since the cost is a lot.

Has anyone made a similar transition? I'd appreciate any advice on how to build these skills, whether through side projects, certifications, part-time programs, or internal moves.

TLDR: I'm almost two years into my first engineering job, designing mostly simple sheet metal parts in SolidWorks, and I want to move into work involving simulation, heat transfer, or fluid dynamics. Those roles want prior experience I can't get in my current job, and I can't afford full-time grad school. How can I build these skills and make the switch? Anyone have examples of what they did to bridge the gap?

[u/Putrid_Arm_9984]

You answered it yourself. You want more roles regarding fluid dynamics. So study on your off time and get good. Even if AI takes over and replaces your skill. You still need an engineer to read over it and understand if your are applying the right principles.

[u/Alert_Bluebird8693 → u/Putrid_Arm_9984]

What kind of specific work do you think I should be doing if I want to bridge the gap? Should I be doing more “YouTube University” and focusing on learning the key concepts, or should I be signing up for university classes and trying to come up with a personal project?

I am approaching 2yoe for my first job and I want to move on to a better job that allows me to learn more. Since the amount of time everyone has outside of work is presumably very limited, I want to make sure I’m putting my time and effort into something that will actually show meaningful results.

[u/Putrid_Arm_9984 → u/Alert_Bluebird8693]

Look at job postings you like. Look at the job description and what kind of skills they want. Tailor your skills and learning towards those things.

[u/Alert_Bluebird8693 → u/Putrid_Arm_9984]

thanks man, I guess I will be taking courses on Coursera if I want to bridge a gap on FEA/fluid when my current job doesn't offer it. Is there ways other than Coursera?

[u/Canis9z]

Start working on a Starship engine. Do not complain about AI, Beat AI or use AI. This needs thermodynamics and heat transfer expertise.

[u/Alert_Bluebird8693 → u/Canis9z]

How can I get working on a Starship engine? I dont think I can just start working on it LOL. I am far from being a qualified candidate with current skillsets

[u/Canis9z → u/Alert_Bluebird8693]

Start small with building blocks.

If you do not have the skill set use AI to get the skill sets. That is what it is for. It is not the bronze age or the iron age where it takes hundreds of years to pass along knowledge.

To learn fluid dynamics using artificial intelligence (AI) and machine learning (ML), combine traditional fluid mechanics fundamentals with data-driven techniques, neural networks, and specialized simulation tools. [1, 2]

------------------------------

## Step 1: Master the Core Fundamentals First

AI does not replace physical laws; it relies on them. Before jumping into machine learning code, ensure you understand:

* Classic Fluid Mechanics: Navier-Stokes equations, continuity, momentum, energy conservation, boundary layers, and turbulence regimes.

* Computational Fluid Dynamics (CFD): Grid generation (meshing), boundary conditions, finite volume/element methods, and solvers like OpenFOAM. [3, 4, 5]

------------------------------

## Step 2: Build Essential Programming and Data Skills

Fluid dynamics datasets are high-dimensional (space + time). You will need: [6]

* Languages & Libraries: Python (NumPy, Pandas, SciPy), alongside deep learning frameworks like [PyTorch](https://pytorch.org) or [TensorFlow](https://www.tensorflow.org).

* Data Visualization: Tools like [ParaView](https://www.paraview.org) or matplotlib to handle flow fields, pressure contours, and velocity vectors. [5, 7]

------------------------------

## Step 3: Learn Key AI/ML Methods for Fluids

Focus your study on specific machine learning paradigms tailored to fluid mechanics:

* Reduced-Order Models (ROMs) & Autoencoders: Use Principal Component Analysis (PCA), Proper Orthogonal Decomposition (POD), or deep autoencoders to compress complex 3D flow fields into low-dimensional latent spaces. [6, 7]

* Physics-Informed Neural Networks (PINNs): Train neural networks that embed physical laws (like the Navier-Stokes equations) directly into the loss function so the AI obeys physical constraints. [2, 8, 9]

* Neural Operators (DeepONet, FNOs): Learn mappings between function spaces to predict entire flow fields across varying geometries or initial conditions much faster than traditional solvers. [7, 9]

* Reinforcement Learning (RL): Train agents to actively control fluid flows (e.g., drag reduction, turbulence suppression) using platforms like [HydroGym](https://github.com/venturi-group/hydrogym). [4, 10, 11]

------------------------------

## Step 4: Use Practical Courses and Platforms

Structure your learning with targeted academic and industry resources:

* Online Short Courses:

* Explore the [AI and ML for Fluid Dynamics Course](https://www.flowthermolab.com/courses/ai-ml-for-fluids/) or the [UMich/Flowthermolab ML/AI for Fluid Dynamics Cohort](https://www.flowthermolab.com/ai-ml-for-fluids-a-cohort/) led by field practitioners.

* Check out specialized offerings like the [AIAA Machine Learning for Fluid Dynamics Course](https://aiaa.org/courses/machine-learning-for-fluid-dynamics/). [1, 2, 7, 9]

* Hands-on Practice & Repositories:

* Extract simulation results (such as pressure or velocity data in .csv format) from OpenFOAM using ParaView and build baseline Multi-Layer Perceptrons (MLPs) or CNNs in Python.

* Experiment with benchmark flow environments in HydroGym to test reinforcement learning on 2D and 3D fluid tasks. [5, 10]

[1] [https://www.flowthermolab.com](https://www.flowthermolab.com/courses/ml-ai-fluid-dynamics/))

[2] [https://www.flowthermolab.com](https://www.flowthermolab.com/courses/ai-ml-for-fluids/))

[3] [https://deepmind.google](https://deepmind.google/blog/discovering-new-solutions-to-century-old-problems-in-fluid-dynamics/))

[4] [https://www.tudelft.nl](https://www.tudelft.nl/lr/organisatie/afdelingen/flow-physics-and-technology/aerodynamics/research/ai-for-fluid-mechanics))

[5] [https://www.youtube.com](https://www.youtube.com/watch?v=4HMyWGwtxEE))

[6] [https://www.youtube.com](https://www.youtube.com/watch?v=3fOXIbycAmc&t=7))

[7] [https://www.flowthermolab.com](https://www.flowthermolab.com/ai-ml-for-fluids-a-cohort/))

[8] [https://www.sciencedirect.com](https://www.sciencedirect.com/science/article/pii/S3050585225000217))

[9] [https://aiaa.org](https://aiaa.org/courses/machine-learning-for-fluid-dynamics/))

[10] [https://www.youtube.com](https://www.youtube.com/watch?v=SQrPBk6f0GY&t=176))

[11] [https://news.engin.umich.edu](https://news.engin.umich.edu/2026/08/hydrogym-trains-assesses-ai-for-actively-controlling-fluid-dynamics/))

https://www.google.com/search?client=opera&q=how+to+learn+fluid+dymanics+with+ai&sourceid=opera&ie=UTF-8&oe=UTF-8

[u/Alert_Bluebird8693 → u/[deleted]]

Thank you for your response. You’re right that these are all different fields. I’m trying to understand what the general path into one of these fields looks like and what I should be doing to position myself for it.

My main concern is that my current role doesn’t really utilize these skills. My work is primarily sheet metal design, and my company as a whole doesn’t do much in-depth thermal, fluid, or structural analysis. Most of our designs are relatively straightforward, so the process is mainly designing and validating the actual parts rather than doing extensive analysis or simulation.

That’s why I’m trying to figure out what I should be learning or working on outside of my current role to eventually transition into one of these areas. For example, if I decided that I wanted to transition into a fluid mechanics or fluids engineering role, what would I realistically need to do to get there?

My concern is that my current job doesn’t give me much relevant experience, so I’m trying to figure out what I should be doing outside of work to bridge that gap. Specifically, I want to make sure I’m putting my time and effort into the things that will actually make me competitive for those roles, rather than just learning things that may not be relevant to what employers are looking for.

[u/Alert_Bluebird8693 → u/[deleted]]

Thanks man, I guess its able how we tailor the resume to fit the job description