Foreword

I still remember the hum of the old computer in the corner of the Universitas Indonesia (UI) lab, its screen flickering as I painstakingly typed out my first FORTRAN code. Those early days were a blend of excitement and frustration. Each simulation I ran felt like unlocking a new secret of the universe, yet the path was often obscured by the limitations of the technology we had. FORTRAN was powerful, but it demanded unwavering precision and patience. Every error message was a puzzle, every successful run a triumph.

By the early 2000s, the landscape of computational physics had changed to C/C++, but not always for the better. Accessing sample codes from textbooks became a formidable challenge. Libraries were filled with books that contained brilliant theories and elegant equations, but the accompanying code? Scarce and scattered. Implementing complex simulations required not just understanding the physics, but also the ingenuity to bridge the gap between theory and practice with minimal resources. It was a time that tested our resilience and creativity, pushing us to find ways to overcome the barriers that stood between us and our computational dreams.

Fast forward to today, and the scene has transformed dramatically. Modern programming languages like Rust have emerged, offering a harmonious blend of flexibility and performance that was unimaginable in my FORTRAN/C++ days. Rust's safety guarantees and efficient concurrency make it an ideal tool for crafting sophisticated simulations. The transition from FORTRAN/C++ to Rust feels like moving from a narrow, well-trodden path to a vast, open landscape where possibilities are limited only by our imagination. With Rust, the once-daunting task of writing and maintaining complex code becomes more manageable, allowing us to focus on what truly matters: the physics itself.

But the revolution doesn't stop there. The rise of Code Large Language Models (LLMs) has been nothing short of a game-changer. These intelligent assistants free us from the arduous task of memorizing endless lines of code and intricate algorithms. Imagine having a collaborator who can generate, debug, and optimize code on the fly, adapting to your needs and accelerating your workflow. This synergy between human insight and machine intelligence opens up new horizons, where our cognitive energy can be directed towards deeper understanding and innovative exploration rather than the mechanical aspects of coding.

In this AI-driven era, we find ourselves at a unique crossroads. The burden of implementing numerical and semi-numerical algorithms is increasingly handled by AI, allowing us to delve deeper into the mysteries of physics. Emerging models like neural networks are beginning to reshape the future of computational physics, offering novel methods for modeling, prediction, and analysis that were once the stuff of science fiction. These advancements not only enhance our capabilities but also inspire us to ask new questions and pursue uncharted territories in our quest for knowledge.

The profound impact of computational advancements on physics is well-illustrated by the legacy of Nobel Prize laureates. While AI itself has yet to be directly recognized with a Nobel Prize in Physics, the integration of advanced computational techniques has been pivotal in groundbreaking discoveries. For instance, the 2020 Nobel Prize in Physics was awarded for groundbreaking work on black hole physics and the discovery of gravitational waves—achievements that relied heavily on sophisticated simulations and data analysis techniques. As AI continues to evolve, it stands poised to become an indispensable tool in the arsenal of physicists, potentially driving the next wave of Nobel-worthy discoveries. Imagine a future where AI not only accelerates research but also uncovers patterns and phenomena that were previously beyond our reach, echoing the transformative impact that past computational innovations have had on the field.

As you embark on your journey through Computational Physics via Rust, I encourage you to embrace the tools and technologies that empower you. Let Rust be the sturdy vessel that carries your simulations, and let AI be the wind that fills your sails, guiding you toward new discoveries. Remember the words of Max Tegmark: “AI has the potential to unlock new realms of understanding in physics, just as physics continues to inspire advancements in AI.” This symbiotic relationship between AI and physics holds the promise of unprecedented breakthroughs, where each field propels the other to greater heights.

To the students and aspiring computational physicists holding this book: your curiosity and passion are your greatest assets. With Rust and AI by your side, you have the means to illuminate the darkest corners of the universe and uncover the elegant patterns that govern it. Embrace this exciting era, where the fusion of robust programming languages, intelligent algorithms, and profound physical insights will drive the next wave of scientific discovery. The future of computational physics is bright, and it awaits your unique contributions.