Dr. Seyed Abolfazl Aghili | machine learning and deep learning | Best Review Paper Award

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Dr. Seyed Abolfazl Aghili | machine learning and deep learning | Best Review Paper Award

lecturer, Siran university of science and technology, Iran

Seyed Abolfazl Aghili is a civil engineer and researcher with expertise in construction engineering and management. He holds a Ph.D. in Civil Engineering from Iran University of Science and Technology (IUST). His research focuses on machine learning, resiliency, and building information modeling (BIM). Dr. Aghili has published several papers in reputable journals and has presented his work at international conferences. He is fluent in Persian and English and has skills in various software, including Python, MS Project, and Autodesk AutoCAD.

Profile

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Education 🎓

Ph.D. in Civil Engineering, Construction Engineering and Management, Iran University of Science and Technology (IUST), 2019-2024 (link unavailable) in Civil Engineering, Construction Engineering and Management, Iran University of Science and Technology (IUST), 2013-2015 (link unavailable) in Civil Engineering, Isfahan University of Technology (IUT), 2009-2013

Experience 💼 

Researcher, Iran University of Science and Technology (IUST), 2019-2024  Graduate Research Assistant, Iran University of Science and Technology (IUST), 2013-2015  Undergraduate Research Assistant, Isfahan University of Technology (IUT), 2009-2013

Awards and Honors🏆

Ranked 5th among 2200 participants in Nationwide University Entrance Exam for Ph.D. program in Iran, 2019 Ranked 2nd among all construction management students in Iran University Science and Technology, 2013-2015 Ranked 220th among 32,663 participants (Top 1%) in Nationwide University Entrance Exam for (link unavailable) program in Iran, 2013

Research Focus

Machine learning and deep learning methods  Resiliency  Building Information Modeling (BIM)  Human Resource Management (HRM)  Decision Making Systems for Project Managers

Publications 📚

1. Artificial Intelligence Approaches to Energy Management in HVAC Systems: A Systematic Review 🤖
2. Data-driven approach to fault detection for hospital HVAC system 📊
3. Feasibility Study of Using BIM in Construction Site Decision Making in Iran 🏗️
4. Review of digital imaging technology in safety management in the construction industry 📸
5. The role of insurance companies in managing the crisis after earthquake 🌪️
6. The need for a new approach to pre-crisis and post-crisis management of earthquake 🌊

Conclusion

Seyed Abolfazl Aghili is an exceptional researcher with a strong academic background, interdisciplinary research experience, and a notable publication record. His teaching and mentoring experience, as well as his technical skills, demonstrate his commitment to education and research. While there are areas for improvement, Dr. Aghili’s strengths make him a strong candidate for the Best Researcher Award.

Marwa Soliman | Big Data Systems | Best Researcher Award

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Ms. Marwa Soliman | Big Data Systems | Best Researcher Award

Senior Research Assistant, Burke Neurological Institute, United States

Marwa Soliman is a driven and accomplished individual pursuing her MCS in Computer Science (Big Data Systems) at Arizona State University. With a strong foundation in computer science and biology, she is passionate about applying her skills to make a positive impact in the field of neuroscience. 🧠

Profile

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Education 🎓

Marwa Soliman is currently pursuing her Master of Computer Science in Big Data Systems at Arizona State University, anticipated to graduate in June 2025. She holds a Bachelor of Arts in Computer Science and Biology from Manhattanville University, graduating Summa Cum Laude with a GPA of 3.91/4.00. 📚

Experience 💼

Marwa Soliman has gained valuable experience as a Senior Research Assistant at the Burke Neurological Institute, Weill Cornell Medicine, since September 2020. She has also worked as a Summer Research Assistant at Manhattanville University and as a Supplemental Instructor and Academic Science and Math Tutor at various institutions. 🧬

Awards and Honors 🏆

Marwa Soliman has received numerous awards and honors, including the Computer Science Department Honors Award, Biology Department Honors Award, Dr. Ruth Paula Alscher Award, Castle Pin Award, Tri-beta Biological Sciences Honors, and Junior Biology Department Award. 🎉

Research Focus 🔍

Marwa Soliman’s research focus lies at the intersection of computer science and neuroscience. She is particularly interested in applying machine learning and data analysis techniques to better understand neurological disorders and develop novel treatments. Her current research involves analyzing high-dimensional biological datasets and developing tools for assessing motion function. 🧠

Publications

1. Analysis of High-Dimensional Biological Datasets using Machine Learning Techniques 📊
2. Development of a Synchronized Feedback System for Neural Activity and Behavior Analysis 📈
3. Automated Data Pipelines for RNA Sequencing Data Analysis 📊
4. Image Analysis and Machine Learning Techniques for Early Detection of Skin Cancer 📸
5. Design and Implementation of a Deep-Learning Algorithm for Early Detection of Skin Cancer 📊

Conclusion

Marwa Soliman’s impressive educational background, extensive research experience, and technical expertise make her an outstanding candidate for the Best Researcher Award. While there are areas for improvement, her strengths and achievements demonstrate her dedication to advancing knowledge and making a positive impact in her field.

Sabum Jung | Smart factory | Best Researcher Award

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Mr. Sabum Jung | Smart factory | Best Researcher Award

Research engineer, Lg energy solution,South Korea

Sabum Jung is a seasoned Data Scientist and Machine Learning Engineer with over 23 years of expertise in predictive modeling, deep learning, and AI-driven optimization. His career spans LG Energy Solution, SK Holdings, and LG Production Engineering Research Institute, where he pioneered AI applications in high-tech manufacturing, including semiconductor, battery, and display industries. A former Military Intelligence Analyst for the U.S. Army, he has authored research papers and books on AI, machine learning, and Industry 4.0. Fluent in English, Korean, and Japanese, he continues to drive AI innovations in industrial applications.

Profile

🎓 Education

Sabum Jung holds a B.A. (3.9/4.5) and an M.S. (4.2/4.5) in Industrial Engineering from Sung Kyun Kwan University, South Korea. His academic journey focused on advanced analytics, AI-driven optimization, and industrial process improvements. His research contributions in artificial intelligence, reliability engineering, and digital transformation have shaped his expertise in machine learning, deep learning, and predictive modeling, positioning him as a leader in AI applications for manufacturing and industrial systems.

💼 Experience

Currently a Data Scientist at LG Energy Solution, Sabum Jung leads AI-driven innovations in virtual metrology, predictive maintenance, and defect analysis. Previously at SK Holdings, he optimized renewable energy predictions, semiconductor material discovery, and AI-powered industrial operations. His 20-year tenure at LG Production Engineering Research Institute saw groundbreaking work in machine learning for smart appliances, battery systems, and industrial automation. His early career as a Military Intelligence Analyst in the U.S. Army honed his analytical prowess, setting the foundation for his AI-driven problem-solving approach.

🏆 Awards & Honors

Sabum Jung has been recognized for his contributions to AI, machine learning, and industrial automation. His accolades include leadership in AI-driven manufacturing optimization, predictive maintenance, and reinforcement learning applications. He has received industry recognition for his research and innovation in deep learning, active learning, and process optimization in high-tech sectors, further cementing his influence in AI-driven industrial advancements.

🔬 Research Focus:

Sabum Jung specializes in AI applications for high-tech manufacturing, focusing on predictive maintenance, virtual metrology, and defect detection. His research spans deep learning, reinforcement learning, and AI-driven industrial process optimization. Notable contributions include renewable energy prediction, semiconductor material discovery, and advanced statistical modeling. His expertise in machine learning has been instrumental in developing AI solutions for smart manufacturing, Industry 4.0, and digital transformation.

Publications

Recent progress of LG PDP: High efficiency & productivity technologies Citations1

Conclusion

Sabum Jung is a strong candidate for the Best Researcher Award, given his vast industry experience, research excellence, and technological contributions to AI and machine learning in manufacturing. Enhancing academic collaborations and increasing research dissemination could further elevate his impact and recognition.

Manar Hamza | Computer Science Data mining | Best Researcher Award

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Dr. Manar Hamza | Computer Science Data mining | Best Researcher Award

professor at  Prince Sattam bin Abd El Aziz University, China

👩‍🏫 Experienced Computer Science Lecturer since 2005 with expertise in data mining, text mining, and information security. 💻 Holds a strong track record in research and academia, leveraging innovation and teamwork. Aims to thrive in challenging, dynamic, and team-oriented environments that foster growth. 🌍 Based in Sudan and Saudi Arabia, dedicated to academic excellence and community impact.

Professional Profiles:

scopus

Education 🎓

Ph.D. in Computer Science from Omdurman Islamic University, Sudan (2018–2021). 🎓 Master’s Degree in Computer Science from Sudan University of Science and Technology (2003–2005). 🎓 B.Sc. in Computer Science from Omdurman Islamic University, Sudan (1995–1999). 📚 Comprehensive training in research skills, academic advising, and IT tools like Mendeley, Latex, and iThenticate.

Experience 🖥️

Lecturer in Computer Science at Prince Sattam bin Abdul-Aziz University, Saudi Arabia (2013–present). 👩‍💼 Supervisor and Coordinator roles in quality, academic advising, and measurement (2014–2020). 🇸🇩 Lecturer at Omdurman Islamic University, Sudan (2005–2012). 👩‍🔬 E-teaching and training specialist with Arab Board experience (2023).

Awards and Honors 🏆

Certificates of Appreciation from PSAU for contributions to quality, development, and academic planning. 🙌 Recognized for voluntary services, including extracurricular activities and technical support for students and staff. ⭐ Esteemed arbitrator in scientific and innovation conferences. 📜 Active contributor to enhancing the learning environment with innovative solutions.

Research Focus 🔍

Data mining, text mining, and information security are core research areas. 📊 Interested in qualitative research, outcome-based education, and e-learning systems. 🌐 Advocates for advancing academic IT tools like Prezi, Mendeley, and iThenticate. 🛡️ Exploring cybersecurity methods and their application in education and industry.

✍️Publications Top Note :

1. Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

Published in: SN Computer Science, 2024, 5(5), 549

Summary: This paper proposes a novel classification model for urban-related tweets using arithmetic optimization integrated with deep learning to support sustainable urban living solutions.

2. Enhancing Traffic Flow Prediction in Intelligent Cyber-Physical Systems

Published in: IEEE Transactions on Consumer Electronics, 2024, 70(1), pp. 1889–1902

Summary: Introduces a Bi-LSTM approach enhanced with a Kalman filter for accurate traffic flow prediction, addressing challenges in intelligent cyber-physical systems.

Citations: 5

3. Deer Hunting Optimization with Deep Learning-Driven Automated Fabric Defect Detection and Classification

Published in: Mobile Networks and Applications, 2024, 29(1), pp. 176–186

Summary: Utilizes the Deer Hunting Optimization algorithm with deep learning to achieve high accuracy in detecting and classifying fabric defects.

Citations: 1

4. Automatic Recognition of Cyberbullying in the Web of Things and Social Media Using Deep Learning Framework

Published in: IEEE Transactions on Big Data, 2024

Summary: Develops a deep learning-based framework to detect and prevent cyberbullying within social media and IoT environments.

5. Artificial Rabbit Optimizer with Deep Learning for Fall Detection in IoT Environment

Published in: AIMS Mathematics, 2024, 9(6), pp. 15486–15504

Summary: Introduces the Artificial Rabbit Optimizer combined with deep learning to enhance fall detection systems for disabled individuals in IoT environments.

Citations: 1

6. Computational Linguistics-Based Arabic Poem Classification and Dictarization Model

Published in: Computer Systems Science and Engineering, 2024, 48(1), pp. 98–114

Summary: Proposes a computational linguistics model to classify Arabic poems and enhance their dictarization process.

7. Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

Published in: Intelligent Automation and Soft Computing, 2024, 38(2), pp. 153–168

Summary: Develops a deep neural network with hyperparameter tuning for effective abstractive summarization of Arabic texts.

Citations: 1

8. Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in IoT Environment

Published in: IEEE Access, 2024, 12, pp. 258–267

Summary: Presents a Chaotic Equilibrium Optimizer combined with deep learning to improve green communication and load prediction in IoT systems.

Citations: 2

9. Land Use and Land Cover Classification Using River Formation Dynamics Algorithm With Deep Learning on Remote Sensing Images

Published in: IEEE Access, 2024, 12, pp. 11147–11156

Summary: Leverages the River Formation Dynamics algorithm integrated with deep learning for efficient land use and land cover classification using remote sensing data.

Citations: 4

10. Prediction of Sleep Quality Using Wearable-Assisted Smart Health Monitoring Systems

Published in: Journal of King Saud University – Science, 2023, 35(9), 102927

Summary: Utilizes wearable technology and statistical data to predict sleep quality, providing insights into personalized smart health monitoring systems.

Citations: 1

Conclusion

The candidate’s extensive experience, academic qualifications, and contributions to computer science, particularly in data mining and information security, make them a strong contender for the Research for Best Researcher Award. With some strategic enhancements to highlight impactful research and global contributions, their profile could exemplify the qualities of an award-winning researcher in computer science.

alain R THIERRY | Data Science and Deep Learning | Excellence in Research

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Prof. alain R THIERRY  | Data Science and Deep Learning | Excellence in Research

Director of Research, INSERM U1194, France

Dr. alain R THIERRY, a distinguished biologist, and cancer researcher, is a Director of Research at INSERM and a key figure at the Institut de Recherche en Cancérologie de Montpellier. With an impressive track record in molecular biology, gene therapy, and cancer research, Dr. alain R THIERRY has held numerous influential positions in academia and the biotechnology sector, including roles at NIH and Georgetown University. A prolific author and scientific leader, they have also founded biotech companies like MedinCell and DiaDx. Dr. alain R THIERRYcontinues to drive innovative therapeutic solutions, recognized by international honors and awards.

Publication Profile

scholar

Education🎓 

2003: Habilitation à Diriger les Recherches (HDR) in Biology-Health, Université Montpellier II 1987: CES in Human Biology (Oncology), Faculté de Médecine Paris-Sud 1986: PhD in Biochemistry, Cellular & Molecular Pharmacology, Université Montpellier II 1983: MSc in Cellular & Molecular Biology, Université de Clermont-Ferrand II 1983: Diplôme d’Ingénieur, Université Clermont-Fd II 1982: BSc in Biological Sciences & Technology, Université Clermont-Fd

Professional Experience💼 

208-present: Director of Research, INSERM, Institut de Recherche en Cancérologie, Montpellier 2001-2007: Associate Professor, Université Montpellier II2003-2004: Director of R&D, MedinCell SA, Montpellier 1997-2000: Scientific Director, Gene Therapy Dept., Biovector Therapeutics 1992-1996: Scientist, Tumor Cell Biology Lab, NCI/NIH, Bethesda 1992-1994: Adjunct Assistant Professor, Lombardi Cancer Institute, Washington DC 1988-1992: Postdoctoral Fellow, Lombardi Cancer Center, Georgetown University

Awards and Honors🏆 

1994: Federal Technology Award, NIH, USA ($10,000) 2002: Prix National de l’Innovation, Ministry of Education and Research, Paris (€300,000) 2016: Grand Prix de l’Innovation Thérapeutique, Fondation B. Denys & FRM, Montpellier (€50,000) 2022: Finalist, Prix Innovation Unicancer 2022: Innovation Award, Montpellier Université Excellence

Research Focus 🔬 

Molecular Oncology: Pioneer in understanding the molecular pathways of cancer and therapeutic gene delivery Gene Therapy: Focus on targeted gene therapy to treat cancers, with expertise in vectors and delivery systems Circulating DNA: Breakthrough research in non-invasive biomarkers for early cancer detection
Therapeutics Innovation: Key developer of novel therapeutic strategies, including drug delivery systems and cancer diagnostics
Collaborative Research: Strong interdisciplinary collaborations in biotechnology and cancer research

Publication  Top Notes

  • Origins, structures, and functions of circulating DNA in oncology
    AR Thierry, S El Messaoudi, PB Gahan, P Anker, M Stroun
    Cancer and Metastasis Reviews, 2016 | 812 citations
  • Clinical validation of the detection of KRAS and BRAF mutations from circulating tumor DNA
    AR Thierry, F Mouliere, S El Messaoudi, C Mollevi, E Lopez-Crapez
    Nature Medicine, 2014 | 735 citations
  • Nomenclature for synthetic gene delivery systems
    PL Felgner, Y Barenholz, JP Behr, SH Cheng, P Cullis, L Huang, AR Thierry
    Human Gene Therapy, 1997 | 652 citations
  • High fragmentation characterizes tumour-derived circulating DNA
    F Mouliere, B Robert, E Arnau Peyrotte, M Del Rio, M Ychou, F Molina, AR Thierry
    PLOS One, 2011 | 627 citations
  • Circulating cell-free DNA: preanalytical considerations
    S El Messaoudi, F Rolet, F Mouliere, AR Thierry
    Clinica Chimica Acta, 2013 | 602 citations

Conclusion:

This individual is highly suitable for the Best Researcher Award. Their long-standing career in oncology research, leadership in both academic and biotech sectors, and recognition through awards place them in an elite category of researchers. Continued engagement in broader interdisciplinary fields and public communication could further elevate their profile. Overall, their qualifications, contributions, and leadership make them a strong candidate for excellence in research awards.

Aaron Brunk | Applied and Numerical Analysis | Best Researcher Award

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Dr. Aaron Brunk | Applied and Numerical Analysis | Best Researcher Award

Dr. Johannes-Gutenberg University, Germany

Dr. Aaron Brunk is a Post-Doc Research Fellow at Johannes Gutenberg-University Mainz, specializing in numerical mathematics under Prof. Dr. Maria M. Lukácová-Medvid’ová. He focuses on thermodynamically consistent fluid modeling, parabolic cross-diffusion system analysis, and structure-preserving method construction. Dr. Brunk completed his PhD with magna cum laude in 2022, studying viscoelastic phase separation. His work includes multiple DFG projects, with roles ranging from PhD student to Principal Investigator. He is an active academic contributor, organizing seminars and workshops, presenting at international conferences, and engaging in research stays and academic self-administration. His current research projects involve variational quantitative phase-field modeling and spinodal decomposition of polymer-solvent systems.

 

Professional Profiles:

Googlescholar

🎓 Education

Nov. 2017 – Feb. 2022: Ph.D. in Mathematics (Dr. rer. nat.), Johannes Gutenberg-University Mainz, GermanyDissertation: Viscoelastic phase separation: Well-posedness and numerical analysisDisputation: 11.02.2022Degree: Magna cum laudeSupervisor: Prof. Dr. Mária M. Lukáčová-Medvid’ováOct. 2015 – Nov. 2017: M.Sc. in Mathematics, Johannes Gutenberg-University Mainz, GermanyThesis: Numerische Behandlung von zeitgebrochenen DiffusionsgleichungenSupervisor: Prof. Dr. Thorsten RaaschOct. 2012 – Oct. 2015: B.Sc. in Mathematics, Johannes Gutenberg-University Mainz, GermanyThesis: Mathematische Modellierung von PhosphorylierungssystemenSupervisor: Prof. Dr. Alan Rendall

🎓 Professional Experience

Feb. 2022 – Present: Post-Doc Research Fellow, Institute of Mathematics, Johannes Gutenberg-University Mainz, GermanyGroup: Numerical MathematicsSupervisor: Prof. Dr. Mária M. Lukáčová-Medvid’ováActivities:🧪 Modelling of thermodynamically consistent complex fluids📊 Analysis of parabolic cross-diffusion systems🔧 Construction of structure-preserving methods for cross-diffusion systems👨‍🏫 Assistant in various tutorials and seminars📚 Independent lecturingNov. 2017 – Feb. 2022: Research Assistant, Institute of Mathematics, Johannes Gutenberg-University Mainz, GermanyGroup: Numerical MathematicsSupervisor: Prof. Dr. Mária M. Lukáčová-Medvid’ováActivities:🧪 Modelling and analysis of viscoelastic phase separation👨‍🏫 Assistant in various tutorials and seminars

📚 Third Party Projects

Sep. 2023 – Aug. 2026: German Research Foundation (DFG) – Principal InvestigatorProject: Variational quantitative phase-field modeling and simulation of powder bed fusion additive manufacturing within the DFG Priority Programme 2256Collaborator: B.-X. Xu, Technical University Darmstadt, Material ScienceFunded Ph.D. positionFeb. 2022 – Feb. 2026: German Research Foundation (DFG) – Postdoctoral ResearcherProject: Spinodal decomposition of polymer-solvent systems within the TRR 146 Multiscale Simulation Methods for Soft Matter SystemsPrincipal Investigators: M. Lukáčová-Medvid’ová, B. DünwegNov. 2017 – Feb. 2022: German Research Foundation (DFG) – Ph.D. studentProject: Spinodal decomposition of polymer-solvent systems within the TRR 146 Multiscale Simulation Methods for Soft Matter SystemsPrincipal Investigators: M. Lukáčová-Medvid’ová, B. Dünweg, H. Egger

✍️Publications Top Note :

Analysis of a Viscoelastic Phase Separation Model

Authors: A Brunk, B Dünweg, H Egger, O Habrich, M Lukáčová-Medvid’ová, …

Journal: Journal of Physics: Condensed Matter 33 (23), 234002, 2021

Citations: 19

Global Existence of Weak Solutions to Viscoelastic Phase Separation Part: I. Regular Case

Authors: A Brunk, M Lukáčová-Medvid’ová

Journal: Nonlinearity 35 (7), 3417, 2022

Citations: 14

Modelling Cell-Cell Collision and Adhesion with the Filament Based Lamellipodium Model

Authors: N Sfakianakis, D Peurichard, A Brunk, C Schmeiser

Journal: arXiv preprint arXiv:1809.07852, 2018

Citations: 10

Global Existence of Weak Solutions to Viscoelastic Phase Separation: Part II. Degenerate Case

Authors: A Brunk, M Lukáčová-Medvid’ová

Journal: Nonlinearity 35 (7), 3459, 2022

Citations: 9

Systematic Derivation of Hydrodynamic Equations for Viscoelastic Phase Separation

Authors: D Spiller, A Brunk, O Habrich, H Egger, M Lukáčová-Medvid’ová, …

Journal: Journal of Physics: Condensed Matter 33 (36), 364001, 2021

Citations: 9

Existence, Regularity and Weak-Strong Uniqueness for the Three-Dimensional Peterlin Viscoelastic Model

Authors: A Brunk, Y Lu, M Lukacova-Medvidova

Journal: arXiv preprint arXiv:2102.02422, 2021

Citations: 9

Chemotaxis and Haptotaxis on Cellular Level

Authors: A Brunk, N Kolbe, N Sfakianakis

Journal: Theory, Numerics and Applications of Hyperbolic Problems I: Aachen, Germany, …

Citations: 4

On Existence, Uniqueness and Stability of Solutions to Cahn–Hilliard/Allen–Cahn Systems with Cross-Kinetic Coupling

Authors: A Brunk, H Egger, TD Oyedeji, Y Yang, BX Xu

Journal: Nonlinear Analysis: Real World Applications 77, 104051, 2024

Citations: 3

Stability and Discretization Error Analysis for the Cahn–Hilliard System via Relative Energy Estimates

Authors: A Brunk, H Egger, O Habrich, M Lukáčová-Medviďová

Journal: ESAIM: Mathematical Modelling and Numerical Analysis 57 (3), 1297-1322, 2023

Citations: 3

Existence and Weak-Strong Uniqueness for Global Weak Solutions for the Viscoelastic Phase Separation Model in Three Space Dimensions

Authors: A Brunk

Journal: arXiv preprint arXiv:2208.01374, 2022

Citations: 3

Relative Energy and Weak–Strong Uniqueness of a Two‐Phase Viscoelastic Phase Separation Model

Authors: A Brunk, M Lukáčová‐Medvid’ová

Journal: ZAMM‐Journal of Applied Mathematics and Mechanics/Zeitschrift für Angewandte …, 2023

Citations: 2

Viscoelastic Phase Separation: Well-Posedness and Numerical Analysis

Authors: A Brunk

Journal: Dissertation, Mainz, Johannes Gutenberg-Universität Mainz, 2022

Citations: 2

Relative Energy Estimates for the Cahn-Hilliard Equation with Concentration Dependent Mobility

Authors: A Brunk, H Egger, O Habrich, M Lukacova-Medvidova

Journal: arXiv preprint arXiv:2102.05704, 2021

Citations: 2

Stability, Convergence, and Sensitivity Analysis of the FBLM and the Corresponding FEM

Authors: N Sfakianakis, A Brunk

Journal: Bulletin of Mathematical Biology 80, 2789-2827, 2018

Citations: 2

Fundamentals of the Oldroyd-B Model Revisited: Tensorial vs. Vectorial Theory

Authors: A Brunk, J Chaudhuri, M Lukacova-Medvidova, B Duenweg

Journal: arXiv preprint arXiv:2308.01326, 2023

Citations: 1

On Uniqueness and Stable Estimation of Multiple Parameters in the Cahn–Hilliard Equation

Authors: A Brunk, H Egger, O Habrich

Journal: Inverse Problems 39 (6), 065002, 2023

Citations: 1

A Second-Order Fully-Balanced Structure-Preserving Variational Discretization Scheme for the Cahn-Hilliard Navier-Stokes System

Authors: A Brunk, H Egger, O Habrich, M Lukacova-Medvidova

Journal: arXiv preprint arXiv:2209.03849, 2022

Citations: 1

Structure-Preserving Approximation of the Cahn-Hilliard-Biot System

Authors: A Brunk, M Fritz

Journal: arXiv preprint arXiv:2407.12349, 2024

Error Analysis for a Viscoelastic Phase Separation Model

Authors: A Brunk, H Egger, O Habrich, M Lukacova-Medvidova

Journal: arXiv preprint arXiv:2407.01803, 2024

Nonisothermal Cahn-Hilliard Navier-Stokes System

Authors: A Brunk, D Schumann

Journal: arXiv preprint arXiv:2405.13936, 2024