Lucas Brunel | Applied Mathematics | Best Paper Award

Posted on by ScienceFather

Mr Lucas Brunel | Applied Mathematics | Best Paper Award

PhD Student, ONERA, France

Lucas Brunel is a dedicated PhD student at ONERA, Université Paris-Saclay, and CNRS LIMOS, where he focuses on advancing multi-fidelity surrogate models and uncertainty quantification for aerospace applications. With a strong background in mechanical and systems engineering, Lucas collaborates with leading researchers to push the boundaries of computational mechanics and aerospace design optimization. His work is published in prestigious journals and conferences, emphasizing his expertise in surrogate modeling and active learning.

PROFILE

Google Scholar

Orcid

STRENGTHS FOR THE AWARD

  1. Innovative Research Contributions: Lucas Brunel’s work on multi-fidelity surrogate models and uncertainty quantification is at the forefront of applied mathematics and aerospace design optimization. His research addresses complex challenges in computational mechanics, showcasing originality and practical relevance.
  2. Published Works: His publication in the prestigious journal Computer Methods in Applied Mechanics and Engineering and presentations at international conferences like ECCOMAS highlight his ability to produce high-impact research.
  3. Collaborative Excellence: Working with renowned institutions such as CNRS LIMOS, ONERA, and ETH Zürich, Lucas demonstrates interdisciplinary collaboration and a strong network in the scientific community.
  4. Practical Applications: The focus on aerospace vehicle design makes his research highly applicable and impactful in both academia and industry.

AREAS FOR IMPROVEMENTS

  1. Broader Application Scope: Expanding his research to other industries beyond aerospace could further enhance the versatility and applicability of his work.
  2. Increased Visibility: Engaging in outreach activities like workshops, webinars, or community-driven platforms could amplify his visibility in the scientific community.
  3. Impact Metrics: Continuing to build a higher citation count and co-authoring papers with industry professionals could solidify his standing as a leading researcher.

EDUCATION

  • PhD in Applied Mathematics (2022 — Ongoing): ONERA, Université Paris-Saclay & CNRS LIMOS (EMSE, UCA), France.
    • Thesis: Multi-fidelity based mesh uncertainty propagation applied to aerospace vehicle design.
    • Supervisors: Rodolphe Le Riche, Bruno Sudret, Mathieu Balesdent, Loïc Brevault.
  • Diplôme d’Ingénieur in Mechanical Engineering (2017 — 2022): Université de Technologie de Compiègne, France.
  • Master of Science in Systems Engineering (2021 — 2022): Université de Technologie de Compiègne, France.

EXPERIENCE

  • PhD Researcher (2022 — Ongoing): Conducting innovative research on multi-fidelity surrogate modeling and uncertainty quantification for aerospace design at ONERA and CNRS LIMOS.
  • Engineering Projects (2017 — 2022): Worked on mechanical and systems engineering projects during undergraduate and graduate studies, emphasizing computational simulations and design optimizations.
  • Collaborative Research: Engaged with top institutions like ETH Zürich for advancing methodologies in applied mechanics.

AWARDS AND HONORS

  • Recipient of the Best Poster Award at the 9th European Congress on Computational Methods in Applied Sciences and Engineering (2024).
  • Recognized for contributions to the Computer Methods in Applied Mechanics and Engineering Journal.
  • Recipient of a PhD Fellowship for interdisciplinary research in aerospace systems design.

RESEARCH FOCUS

Lucas Brunel specializes in multi-fidelity surrogate modeling, uncertainty quantification, and active learning techniques. His primary aim is to optimize computational methods for aerospace vehicle design. His research integrates functional output simulators, mesh uncertainty propagation, and high-dimensional data modeling to improve predictive accuracy and efficiency in engineering applications.

PUBLICATION TOP NOTES

  • A Survey on Multi-Fidelity Surrogates for Simulators with Functional Outputs: Unified Framework and Benchmark 📖
  • Uncertainty Quantification Oriented Active Learning for Surrogates of Simulators with Functional Outputs 📊
  • A Review of Multi-Fidelity Surrogate Models for High Dimensional Field Outputs 📜

CONCLUSION

Lucas Brunel is a strong contender for the Best Researcher Award due to his innovative and impactful contributions to surrogate modeling and uncertainty quantification. His academic rigor, collaborative efforts, and practical focus on aerospace design optimization establish him as an emerging leader in his field. While broadening the application scope and increasing his visibility could further strengthen his case, his current achievements make him a deserving candidate for this recognition.

Qi Meng – Mathmatics – Best Researcher Award

Posted on by ScienceFather

Qi Meng - Mathmatics - Best Researcher Award

Chinese Academy of Sciences - China

AUTHOR PROFILE

SCOPUS

🧠 INTELLIGENT COMPUTING AND MACHINE LEARNING RESEARCH

Qi Meng is a leading researcher in the field of intelligent computing and machine learning, with a strong focus on applying these techniques to complex physical systems. One of his most significant projects from 2021 to 2024 explored using data-driven models to enhance physical system modeling and simulation. His innovative introduction of LorentzNet, constrained by physical priors, has gained recognition in high-energy physics applications such as Jet Tagging, with numerous citations and praise from prestigious journals.

📚 DEEP LEARNING MATHEMATICAL THEORY

In his research from 2018 to 2021, Qi delved into the mathematical foundations of deep learning, proposing groundbreaking optimization methods like G-SGD and adaptive training techniques such as Path-BN. His work on Power-law dynamics has further advanced understanding of how optimization algorithms impact the regularization effects in deep learning. This research has been featured in top-tier machine learning conferences, including ICML, NeurIPS, and ICLR.

⚛️ PHYSICAL SYSTEM MODELING AND SIMULATION

Qi’s work on accelerating the solution of partial differential equations (PDEs) through machine learning, including methods such as DRVN and DLR-Net, has provided robust solutions to Navier-Stokes equations and stochastic models. His papers on this topic have been published in leading journals like Physical Review E and Physics of Fluids, and his work at the AAAI-23 conference was acknowledged as technically groundbreaking in the AI sub-field.

🌍 DISTRIBUTED MACHINE LEARNING ALGORITHMS

During his time at Microsoft Research Asia from 2015 to 2017, Qi contributed to the development of distributed machine learning algorithms. His work on the LightGBM and DC-ASGD algorithms has had a significant impact, with LightGBM accumulating over 12,400 citations. These tools are widely used in large-scale machine learning applications, enhancing parallel optimization and distributed decision-making processes.

🔢 DEEP LEARNING APPLICATIONS IN PARTIAL DIFFERENTIAL EQUATIONS

Qi’s innovative research on the application of deep neural networks to solve complex stochastic PDEs has brought forth new methods such as NeuralStagger, which uses spatial-temporal decomposition to accelerate physical simulations. His work has been presented at major conferences such as ICML, further cementing his role as a leader in this intersection of deep learning and physics.

📊 DATA-DRIVEN MODELING AND OPTIMIZATION

Throughout his career, Qi has been at the forefront of applying machine learning to solve real-world problems. His work on optimizing neural network path space and addressing the generalization theory in deep learning has opened new avenues in AI research. His contributions to algorithms like G-SGD and innovations in regularization have made significant waves in the AI community.

🎓 PIONEERING CONTRIBUTIONS TO MACHINE LEARNING THEORY

Qi Meng's pioneering contributions, particularly in the development of distributed machine learning and optimization techniques, have earned him widespread recognition. His collaborative work with other renowned researchers continues to push the boundaries of what machine learning can achieve, leading to publications in prestigious venues like KDD, ACL, and AAAI.