Petro Maksymovych’s Post

Exciting News! I am thrilled to share that my recent research has been published in Physical Review Research! This journey has been filled with unexpected challenges and learning experiences. I am very grateful to my advisor for all his support and guidance. The paper focuses on the collective motions of semiflexible filaments that self-propel in quasi-two-dimensions. Through Brownian dynamics simulations, we gained insights into the aligning mechanisms at work in gliding assay systems. Our findings reveal the conditions under which long-range orientational order emerges and how the energy cost of filament overlap and flexibility influence the symmetry of the order. Interestingly, our model predicts that long-range polar order is the only active dynamical steady state in systems with finite filament rigidity. Seeing the paper finally come out is incredibly rewarding. Thank you all for your support! Here’s to many more milestones ahead! 📚✨ Check it out here: https://lnkd.in/eX6hG5fK

This is what I think about the evolution of experimental research models: from small animals to advanced organ-on-a-chip and 3D bioprinting. We are soon approaching a world where quantum computing can offer precise simulations of human biological responses without the need for experimentation. I'm so excited to be doing research at such an amazing time in human history, and the image I created attempts to capture this incredible trajectory. How many years do you think it will take to reach this point?

Perfect ending for my Ph.D. journey, as my final research article, 'Classifying muscle performance of junior endurance and power athletes using machine learning' had just been published in the 'International Journal of Biomedical Engineering and Technology'. This article explains the ability of machine learning model to make predictions on muscle performance between different types of athletes. It also includes the details for each processing part such as data cleaning, feature extraction, and classification process. Alhamdulillah, grateful for all the support and looking forward to new challenges ahead!

🚀 Excited to share my latest research on electron orbitals! My paper, "Electron Orbitals Are Not Probability Clouds: They Are Holographic Planes," offers a fresh perspective on the nature of quantum mechanics. By proposing that electron orbitals are holographic planes defined by the fine-structure constant and relativistic principles, this framework bridges quantum mechanics and relativity. Key Insights: A deterministic model for quantum phenomena like quantized energy levels and interference. Explains the electron's spacetime geometry and electromagnetic interactions. Opens the door to unifying quantum mechanics and relativity with testable predictions. https://lnkd.in/g6iY6wMa I'm eager to hear your thoughts and engage in discussions about how this could reshape our understanding of quantum systems. Feel free to share or connect if this resonates with your interests!

Marie Curie got kicked out of her lab 12 times before changing science forever. Why? Because her mind worked the same way yours does. She couldn't stop seeing connections everyone else missed. Couldn't focus on "just chemistry" or "just physics" when her brain kept mapping patterns across both. Sound familiar? In a world obsessed with single-track thinking, she turned every rejection into rocket fuel. While others fought to understand elements one at a time, she was spotting constellations that would rewrite our understanding of matter itself. Here's what nobody tells you about breakthroughs... They rarely come from minds that color inside the lines. That X-ray machine that saved countless lives during WWI? It existed because Curie's "scattered" thinking connected pure research to battlefield medicine in ways nobody had imagined. But the real power move? When they told her women couldn't do groundbreaking science, she didn't just prove them wrong. She won Nobel Prizes in two different fields. Because constellation thinkers don't just solve problems – we transform entire industries by seeing patterns others miss. Next time someone tells you to focus on one thing at a time, remember Curie. She didn't change the world by thinking smaller. She revolutionized it by letting her mind create maps others couldn't even see yet. You're not scattered. You're not unfocused. You're mapping constellations that will light up the future. What patterns are you seeing that others keep missing? What constellation is your mind mapping that could change everything?

Glad to share that our IITH-Deakin PhD scholar, Ms Gaijinliu Gangmei, presented her research towards mimicking the phenomenon of materials microstructural evolution using machine learning in the 13th International Conference on Engineering and Innovative Materials (ICEIM 2024) in Tokyo, Japan. Through her research, our endeavor towards digital twin construction for complicated process of evolution dynamics of precipitate growth is taking shape.  More about her work ... Materials microstructural evolution can be effectively investigated with physics-based models, such as phase-field modeling. Nevertheless, the need to generate fine mesh systems in order to obtain numerical solutions of complex partial differential equations(PDEs) systems makes it computationally expensive. Therefore, the focus of this work is on Fourier Neural Operators (FNO), a quick and generalizable machine learning model that serves as a surrogate model. In this study, we have demonstrated the capability of FNO to learn the dynamics of precipitate growth. For interpolation settings, FNO could accurately predict the two coupled phase-field variables(c and η) which represent the evolutionary state of the precipitate growth. It could also predict microstructure evolutions based on unseen initial conditions in extrapolation settings that is, outside the training set’s distribution of initial conditions. Congratulations Gaijin 👏

The first results of my PhD project are now publically available as a preprint! See https://lnkd.in/ePMgsMCN. Much like neuroscientists require a solid understanding of the connections between each neuron in order to understand cognition, we need a solid understanding of communication pathways between individuals in order to understand the collective intelligence of groups. One major difference from brains is that individuals can move around in an environment, constantly changing communication networks. We simulated this dynamic effect of movement in an environment on network structure, by simulating a population of agents moving around in a 2D coordinate space and forming temporary connections while within a specified range. These simulations allowed us to understand the formation of communication networks under different spatial circumstances, quantifying the impact of communication range, population size and environment size on the properties of the resulting networks. This allows to us understand part of human communication networks without assuming individual connection preferences, focusing instead on the basic fact that people exist and communicate in an environment. (Model code and instructions are publically available on github: https://lnkd.in/e3vunKfU )

Johns Hopkins APL honored nearly 180 staff members for their exceptional contributions in 2023 during the lab's annual Achievement Awards ceremony. During the virtual event, staff members received awards for outstanding work in areas such as publications, inventions and mission accomplishments. 🏅 Learn more about the 28 winning entries: https://jhuapl.link/nwg #JHUAPL | #STEM | #Innovation | #ScienceAndTechnology | #ScienceRecognition

QCi was thrilled to learn that U.S. scientist John Hopfield and British-Canadian scientist Geoffrey Hinton were the well-deserved recipients of this year’s Nobel Prize in Physics for their foundational work in machine learning! Their groundbreaking work has long inspired the QCi team and continues to play a pivotal role in QCi’s technology and innovations. For example, QCi’s recently developed photonic optimization solver, the Dirac-3, draws directly from Dr. Hopfield and Hinton’s work. The cost function that Dirac-3 natively instantiates is a higher-dimensional version of the equation they pioneered. With the advances in photonic technology that QCi is leading, the QCi team can efficiently implement these higher-order terms in the cost function, enabling the team to solve much more complex problems. Stay tuned for more exciting announcements from QCi, but for now, let's take the time to recognize and celebrate the significant achievements of Dr. Hopfield and Hinton in shaping the future of machine learning! #NobelPrize #NobelPrize2024 #physics #computerscience #machinelearning #AI #artificialneuralnetworks #pioneers #breakthroughs #groundwork #research #discoveries #inventions #revolutionary #recognition #inspiration #futureofmachinelearning #QCi #Dirac3  #quantummachine #quantumsystems #quantumtechnologies #qudits #photons #optics

The high dimensional encoding, the ability to solve mixed integer and continuous variable problems, and all-to-all connectivity make Dirac-3 a unique and promising optimization machine.