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Hi

kmfdag

Is it possible to get into CFD postgrad with a degree in computer science and mathematics?

Superb interview. It is good to see the social fun side of the cfd experts as well

🌎My Personal Website: jousefmurad.com 👉 Take your Marketing to the next level: theapexconsulting.com

hello, thank you for this!

this was very inspiring, subscribed

Insightful 👍

Josefine - we would make beautiful children, as for Engineers not having imagination- look up FreeCAD Ch. Imagine.

I would think Quantum processors coupled with AI will be game changing for Computational Fluid Dynamics, as Quantum processors excel in simulation, particularly particle simulation, and AI I would believe would be excellent at interpreting design results and rapidly prototyping 3d models for wing shapes, props, turbofans etc. and be able to run the fluid simulations faster than real time on a quantum processor.

nice video, very clear !

Can you start a series on coding or road map for use of Deep learning & machine learning in CFD

Insightful

Would you recommend a specialization in computational engineering as a mechanical engineering major?

Thank you for the informative content. I greatly appreciate the enormous effort you put into it.

This needs more views

Hey am working on a project in which I have to generate 1 crore mesh approximately. Best way to increase simulation speed? Meshing is also taking lot of time.

BEST

Very nicely explained, that too in just 2 mins. Thankyou sir

According to multiple internet sources by upgrading to a Makita it is possible to achieve total quantum entanglement.

'Promosm' 😏

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At th e end of the day - WHICH DAY? Because AI gets better every year and it WILL replace engineers - AGI then ASI can do that by definition. The only question is WHEN, not WHETHER. And accountability - we already know AI is better in many fields - where is the illusion that it will not be accountable, EVER, coming from?

00:00:00 In this section of the podcast, James Shaw discusses the importance of starting from the fundamentals when teaching complex topics like simulation, which involves the pillars of fluid dynamics, mathematics, and computer science. He mentions that old facts from 20 years ago are now misconceptions and emphasizes the need to build complexity from the basics. Shaw also talks about the limitations of simulation experts who may not have access to test data or may not be able to apply their skills to different industries. He encourages people to use social media, such as LinkedIn, to provide feedback and engage with the simulation community. Shaw shares his personal background with simulation, explaining how it unlocked a new level of understanding and insight during his college years and became a crucial learning tool throughout his career. 00:05:00 In this section of the podcast, James Shaw discusses his background in the simulation industry, which spans over 20 years as an end user and trainer. He recalls collaborating with others in the field and developing curriculum together. Shaw reflects on the past and present of simulation learning, acknowledging that while the good old days had their challenges, such as limited resources and the need to learn the hard way, today's students are fortunate to have an abundance of resources at their disposal. He ponders whether the traditional approach of learning the hard way is superior to the current, easily accessible methods. Shaw also shares his experiences of learning simulation in the early 2000s, where finding resources and mentors was more difficult. He describes the challenges of working with simulation tools back then, including the lack of flexibility and the time-consuming process of troubleshooting and iterating models. Today, Shaw is surrounded by an abundance of information and resources, making the learning process more accessible and efficient. 00:10:00 In this section of the podcast, James Shaw discusses the advancements in accessing simulation knowledge compared to the past, where waiting for vendor responses could take days. He emphasizes the abundance of available knowledge today and the importance of a solid foundation in simulation education. Shaw mentions the concept of "scaffolding," which involves building knowledge on layers, starting with fundamentals. He shares his approach to teaching simulation by setting realistic expectations, focusing on troubleshooting, and using problem-based learning (PBL) methods. PBL encourages students to apply their knowledge through projects and hands-on experiences. Shaw also reflects on the importance of overcoming challenges in the learning process. 00:15:00 In this section of the "Become a Simulation Expert" podcast, James Shaw discusses the role of guesswork in engineering simulations and the importance of learning to estimate parameters. He explains that some simulations don't have definitive answers and engineers must learn to make informed guesses. Shaw also talks about the open project sessions in his classes where students can apply their knowledge creatively and learn from each other. He emphasizes the importance of understanding that misconceptions about simulation tools, such as long solve times, are based on outdated facts and should be challenged. 00:20:00 In this section of the podcast, James Shaw discusses common misconceptions about simulation and how to avoid them. He mentions the misconception that simulation takes too long to solve and that it's hard to learn. Shaw encourages being open-minded and checking oneself to overcome these misconceptions. He also talks about the democratization of simulation and how anyone can learn it, but becoming an expert still requires putting in the work. Shaw emphasizes that the simulation industry is healthy and continuously growing, despite being competitive. 00:25:00 In this section of the podcast, James Shaw discusses the competitive nature of the simulation industry and the importance of becoming an expert in the field. He acknowledges that the industry is crowded and competitive but emphasizes that it's not a zero-sum game, meaning that everyone can profit from the market. Shaw suggests that instead of avoiding the competition, individuals should focus on upscaling themselves faster and more effectively by solving complex cases and utilizing the right resources. He also touches upon the concept of expertise and the idea that being an expert can depend on the context and the specific industry or field one is in. Shaw encourages individuals who are interested in entering the simulation industry to continue learning and exploring their curiosity in areas such as fluid dynamics, mathematics, and computer science, as there are countless opportunities to make significant contributions to the field. 00:30:00 In this section of the podcast, James Shaw discusses the distinction between simulation experts and generalists. He explains that while experts are highly proficient in using simulation tools and software in their specific field, they may not be able to adapt to new industries or ways of using the same tools. Generalists, on the other hand, may not be simulation experts but have a broad understanding of various industries and can use simulation as one of their tools. Shaw suggests that in today's world, where simulation is just one of many tools used in engineering, it's essential to be a generalist with a good understanding of simulation and the ability to learn and adapt to new technologies and industries. Shaw recommends becoming a T-shaped personality, with deep expertise in one area and a broad understanding of various related fields. 00:35:00 In this section of the podcast, James Shaw shares his advice for becoming an expert in a specific area, using the example of simulation expertise in product development. He suggests finding something one is interested in or good at, and becoming an expert in that field before expanding to new areas. Shaw uses the example of crash safety and human factors in simulation, emphasizing the importance of mastering the unique physics and material properties at small time scales and high velocities. He also discusses how focusing on one area led him to become known as the "simulation guy," and how ANSYS's large product portfolio allowed him to learn various aspects of AI, ML, and different types of simulations. Shaw's experience with ANSYS began in college when he learned the APDL language and scripted physics, and he was impressed by the ability to code the world with FEA. He notes that people still underestimate the range of ANSYS products and capabilities. 00:40:00 In this section of the "Become a Simulation Expert" podcast, James Shaw discusses the current state of the simulation software market, with a focus on the consolidation of companies and the benefits and drawbacks of this trend. He explains that larger companies are buying up smaller simulation software developers, resulting in a larger pool of available simulation types for users to explore. Shaw encourages those interested in simulation to learn about various companies and their offerings, as exposure to different approaches can lead to better problem-solving skills. He also highlights the importance of user feedback and the role it plays in shaping software development, as well as the advantages of being an end user in the current market. Shaw concludes by promoting his training company, Train a Training, which offers consulting and training services for various simulation software. 00:45:00 In this section of the podcast, James Shaw discusses the importance of investing in one's own skill development and learning new things. He suggests various resources such as RUclips, LinkedIn, and in-person instructor-led training classes. Shaw emphasizes that time is a valuable currency and encourages listeners to dedicate at least an hour a day to learning. He also invites listeners to explore simulations offered by Anis or Fast Way for a deeper understanding of the concepts. Shaw concludes by expressing his excitement for future podcast episodes.

I am a big fan of your podcasts . But how to now that your simulation is going fine if there is n nothing to compare ? is it just by experience ? like for example a new product or a new shape of an arifoil , For instance , I have some random geometries maybe without any data to compare , how to know I am on the right way ? ... by the way I am a beginner indeed . Also when he said that its better 3 hours with an expert than 3 days in youtube . what kind of courses he means ? I am able to pay if it can enhance my learning

Mr. Murad. Thank you so much. A beautiful, humane, warm interview. I almost cried at the ending.

Amazing podcast! Can't wait for more episodes with the same guest...

amazing podcast , keep enlightening us Jousef

Lets hit one podcast with you & me on some common grounds for INDIAN SIMULATION EXPERTS, lets hit it someday @Jousef. Shikhar Saraswat this side, eagerly awaiting to hear back & hit the hot list on LINKEDIN 2024

I'm in my third year in industry doing simulation. The amount of resources I have to learn these tools is amazing, like you guys have discussed. One of my new techniques to help accelerate my learning is to upload lengthy user guides to ChatGPT and ask it questions about my particular application. Training chat sessions on tutorial scripts has also helped me tailor my own scripts to my specific problem as my C background is not that strong yet. That's just before I would contact the multiple responsive application engineers at the commercial software company we use. It's truly an amazing time to be a new engineer.

thanks

Thank you for such inspiring words!

Loved th intensity of insights in this video. 😍 Cool animations too!

Hey

To think I found you on LinkedIn and now find you here.

Thank you for not wearing a nose ring.

Any suggestions for an experience to do in this topic (especially soccer free kicks) for an academic research? 🙏🏽

Thanks mate!

Who else is here from Cyprien's blog? A great podcast btw! 👍🏻

Great podcast. Thank you for sharing!

what is that matlab version?

Very well put dicussion. PINNs are not ready yet for industry problems, for the Forward cases at least. The convergence is not guaranteed and then comes the additional burden of hyperparaneter tuning and architecture optimization. We should rather focus its development on exploiting it where conventional solvers fall weak, ie; inverse problems. Integrating Inverse PINNs to conventional numerical solvers in order to accelerate calibration of the model seems the way forward. Thanks for the nice episode Jousef.

nice!

What an interesting application of computer simulations! I love the interdisciplinarity of this project! May I ask, are simulations of this kind commonly used in practice? I could imagine that many medical doctors do not have the time or patience to do detailed numerical analysis, but I might be wrong...

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Not only is she smart and articulate but she might be the prettiest woman I have ever seen

Correct English would be "fewer" persevere and not "less".

Hi Yousuf Murad, you should ask him recommended books or papers in order to comprehend Fluid-Structure Interactions in single domain.

It's sigma xx for the compression cell. not tho xx

What do you think AI will going to take jobs of cae engineers? If yes what a person should acquire skill for future ready to tackle such situation.What do you think AI will going to take jobs of cae engineers? If yes what a person should acquire skill for future ready to tackle such situation.