#numericalanalysis — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #numericalanalysis, aggregated by home.social.
-
Finite Element Analysis rabbit hole.
#PrePoMax works passably on Linux and with youtube tutorials (thanks Indian- and Polish-accent guys) I could actually simulate twisting a rod and do experiments. Easy to use for a total newb!
That informs me where to place strain gauges for one of my projects.
I promise all the random stuff I've been doing ties together, eventually.
-
Finite Element Analysis rabbit hole.
#PrePoMax works passably on Linux and with youtube tutorials (thanks Indian- and Polish-accent guys) I could actually simulate twisting a rod and do experiments. Easy to use for a total newb!
That informs me where to place strain gauges for one of my projects.
I promise all the random stuff I've been doing ties together, eventually.
-
Finite Element Analysis rabbit hole.
#PrePoMax works passably on Linux and with youtube tutorials (thanks Indian- and Polish-accent guys) I could actually simulate twisting a rod and do experiments. Easy to use for a total newb!
That informs me where to place strain gauges for one of my projects.
I promise all the random stuff I've been doing ties together, eventually.
-
Finite Element Analysis rabbit hole.
#PrePoMax works passably on Linux and with youtube tutorials (thanks Indian- and Polish-accent guys) I could actually simulate twisting a rod and do experiments. Easy to use for a total newb!
That informs me where to place strain gauges for one of my projects.
I promise all the random stuff I've been doing ties together, eventually.
-
Finite Element Analysis rabbit hole.
#PrePoMax works passably on Linux and with youtube tutorials (thanks Indian- and Polish-accent guys) I could actually simulate twisting a rod and do experiments. Easy to use for a total newb!
That informs me where to place strain gauges for one of my projects.
I promise all the random stuff I've been doing ties together, eventually.
-
` Our work demonstrates the sampling of an interacting chaotic system performed on a quantum processor of ultracold atoms and opens the door of utilizable quantum computational advantage in simulating Floquet dynamics of many- body systems`
https://journals.aps.org/prx/pdf/10.1103/5mqb-3by1
#computing #quantumComputing #quantum #numericalAnalysis #science #physics
-
` Our work demonstrates the sampling of an interacting chaotic system performed on a quantum processor of ultracold atoms and opens the door of utilizable quantum computational advantage in simulating Floquet dynamics of many- body systems`
https://journals.aps.org/prx/pdf/10.1103/5mqb-3by1
#computing #quantumComputing #quantum #numericalAnalysis #science #physics
-
` Our work demonstrates the sampling of an interacting chaotic system performed on a quantum processor of ultracold atoms and opens the door of utilizable quantum computational advantage in simulating Floquet dynamics of many- body systems`
https://journals.aps.org/prx/pdf/10.1103/5mqb-3by1
#computing #quantumComputing #quantum #numericalAnalysis #science #physics
-
` Our work demonstrates the sampling of an interacting chaotic system performed on a quantum processor of ultracold atoms and opens the door of utilizable quantum computational advantage in simulating Floquet dynamics of many- body systems`
https://journals.aps.org/prx/pdf/10.1103/5mqb-3by1
#computing #quantumComputing #quantum #numericalAnalysis #science #physics
-
` Our work demonstrates the sampling of an interacting chaotic system performed on a quantum processor of ultracold atoms and opens the door of utilizable quantum computational advantage in simulating Floquet dynamics of many- body systems`
https://journals.aps.org/prx/pdf/10.1103/5mqb-3by1
#computing #quantumComputing #quantum #numericalAnalysis #science #physics
-
GNU Octave 11.2.0 porta ottimizzazioni, miglioramenti alla GUI e numerose correzioni per il calcolo numerico avanzato. #GNUOctave #OpenSource #Linux #ScientificComputing #NumericalAnalysis #Software
-
GNU Octave 11.2.0 porta ottimizzazioni, miglioramenti alla GUI e numerose correzioni per il calcolo numerico avanzato. #GNUOctave #OpenSource #Linux #ScientificComputing #NumericalAnalysis #Software
-
GNU Octave 11.2.0 porta ottimizzazioni, miglioramenti alla GUI e numerose correzioni per il calcolo numerico avanzato. #GNUOctave #OpenSource #Linux #ScientificComputing #NumericalAnalysis #Software
-
GNU Octave 11.2.0 porta ottimizzazioni, miglioramenti alla GUI e numerose correzioni per il calcolo numerico avanzato. #GNUOctave #OpenSource #Linux #ScientificComputing #NumericalAnalysis #Software
-
GNU Octave 11.2.0 porta ottimizzazioni, miglioramenti alla GUI e numerose correzioni per il calcolo numerico avanzato. #GNUOctave #OpenSource #Linux #ScientificComputing #NumericalAnalysis #Software
-
oh no! one of the greats... i still use matlab as my favorite language :)
`We are saddened to share that Cleve Moler passed away on May 20, 2026, at the age of 86 at his home surrounded by his family. Cleve was chief mathematician and cofounder of MathWorks and the author of the first version of MATLAB.`
https://www.mathworks.com/company/aboutus/founders/clevemoler.html
#CleveMoler #LinearAlgebra #software #NumericalAnalysis #MatrixComputations
-
oh no! one of the greats... i still use matlab as my favorite language :)
`We are saddened to share that Cleve Moler passed away on May 20, 2026, at the age of 86 at his home surrounded by his family. Cleve was chief mathematician and cofounder of MathWorks and the author of the first version of MATLAB.`
https://www.mathworks.com/company/aboutus/founders/clevemoler.html
#CleveMoler #LinearAlgebra #software #NumericalAnalysis #MatrixComputations
-
oh no! one of the greats... i still use matlab as my favorite language :)
`We are saddened to share that Cleve Moler passed away on May 20, 2026, at the age of 86 at his home surrounded by his family. Cleve was chief mathematician and cofounder of MathWorks and the author of the first version of MATLAB.`
https://www.mathworks.com/company/aboutus/founders/clevemoler.html
#CleveMoler #LinearAlgebra #software #NumericalAnalysis #MatrixComputations
-
oh no! one of the greats... i still use matlab as my favorite language :)
`We are saddened to share that Cleve Moler passed away on May 20, 2026, at the age of 86 at his home surrounded by his family. Cleve was chief mathematician and cofounder of MathWorks and the author of the first version of MATLAB.`
https://www.mathworks.com/company/aboutus/founders/clevemoler.html
#CleveMoler #LinearAlgebra #software #NumericalAnalysis #MatrixComputations
-
oh no! one of the greats... i still use matlab as my favorite language :)
`We are saddened to share that Cleve Moler passed away on May 20, 2026, at the age of 86 at his home surrounded by his family. Cleve was chief mathematician and cofounder of MathWorks and the author of the first version of MATLAB.`
https://www.mathworks.com/company/aboutus/founders/clevemoler.html
#CleveMoler #LinearAlgebra #software #NumericalAnalysis #MatrixComputations
-
Start here: Standard-Slope Integration (SSI)
A new, first-of-its-kind class of derivative-driven integration operators built solely from slope information.
If you’re new to my work, this post links to the full SSI series—a structured overview of a first-of-its-kind, derivative-driven integration operator built on structural iteration invariants and slope-based reconstruction. The recap summarizes all seven posts in order and provides the conceptual foundation for understanding SSI.
Series recap:
https://mathstodon.xyz/@BlueNovaX/116523359117197532
Repository with details and examples:
https://github.com/BlueNovaX/standard-slope-integration
#numericalanalysis #scientificcomputing #mathematics #integration #StandardSlopeIntegration #SSI
-
Standard‑Slope Integration (SSI) — Post #3: Failure-mode robustness
A new, first-of-its-kind class of derivative-driven integration operators built solely from slope information.
Classical integration methods often fail when the integrand is discontinuous, poorly conditioned, or structurally misaligned with area-based accumulation. SSI approaches these cases differently: its derivative-driven structure and step-to-step invariants prevent the amplification and drift that destabilize classical formulations.
This makes SSI effective in cases where traditional quadrature becomes unstable, unreliable, or fails outright—not by modifying classical methods, but by using a fundamentally different reconstruction principle.
#numericalanalysis #scientificcomputing #mathematics #integration #StandardSlopeIntegration #SSI
-
An excellent introduction to #quantization used for #LLMs 👌🏽:
“Quantization From The Ground Up”, Sam Rose, Ngrok (https://ngrok.com/blog/quantization).
On HN: https://news.ycombinator.com/item?id=47519295
#AI #Math #FloatingPoint #NumericalAnalysis #Numbers #NeuralNetworks #Precision #Accuracy
-
An excellent introduction to #quantization used for #LLMs 👌🏽:
“Quantization From The Ground Up”, Sam Rose, Ngrok (https://ngrok.com/blog/quantization).
On HN: https://news.ycombinator.com/item?id=47519295
#AI #Math #FloatingPoint #NumericalAnalysis #Numbers #NeuralNetworks #Precision #Accuracy
-
An excellent introduction to #quantization used for #LLMs 👌🏽:
“Quantization From The Ground Up”, Sam Rose, Ngrok (https://ngrok.com/blog/quantization).
On HN: https://news.ycombinator.com/item?id=47519295
#AI #Math #FloatingPoint #NumericalAnalysis #Numbers #NeuralNetworks #Precision #Accuracy
-
An excellent introduction to #quantization used for #LLMs 👌🏽:
“Quantization From The Ground Up”, Sam Rose, Ngrok (https://ngrok.com/blog/quantization).
On HN: https://news.ycombinator.com/item?id=47519295
#AI #Math #FloatingPoint #NumericalAnalysis #Numbers #NeuralNetworks #Precision #Accuracy
-
`Various software efforts embrace the idea that object oriented programming enables a convenient implementation of the chain rule, facilitating so-called automatic differentiation via backpropagation. Such frameworks have no mechanism for simplifying the expressions (obtained via the chain rule) before evaluating them. As we illustrate below, the resulting errors tend to be unbounded.`
https://arxiv.org/abs/2305.03863
#calculus #software #numericalAnalysis #automaticDifferentiation #uncertainty
-
`Various software efforts embrace the idea that object oriented programming enables a convenient implementation of the chain rule, facilitating so-called automatic differentiation via backpropagation. Such frameworks have no mechanism for simplifying the expressions (obtained via the chain rule) before evaluating them. As we illustrate below, the resulting errors tend to be unbounded.`
https://arxiv.org/abs/2305.03863
#calculus #software #numericalAnalysis #automaticDifferentiation #uncertainty
-
`Various software efforts embrace the idea that object oriented programming enables a convenient implementation of the chain rule, facilitating so-called automatic differentiation via backpropagation. Such frameworks have no mechanism for simplifying the expressions (obtained via the chain rule) before evaluating them. As we illustrate below, the resulting errors tend to be unbounded.`
https://arxiv.org/abs/2305.03863
#calculus #software #numericalAnalysis #automaticDifferentiation #uncertainty
-
`Various software efforts embrace the idea that object oriented programming enables a convenient implementation of the chain rule, facilitating so-called automatic differentiation via backpropagation. Such frameworks have no mechanism for simplifying the expressions (obtained via the chain rule) before evaluating them. As we illustrate below, the resulting errors tend to be unbounded.`
https://arxiv.org/abs/2305.03863
#calculus #software #numericalAnalysis #automaticDifferentiation #uncertainty
-
`Various software efforts embrace the idea that object oriented programming enables a convenient implementation of the chain rule, facilitating so-called automatic differentiation via backpropagation. Such frameworks have no mechanism for simplifying the expressions (obtained via the chain rule) before evaluating them. As we illustrate below, the resulting errors tend to be unbounded.`
https://arxiv.org/abs/2305.03863
#calculus #software #numericalAnalysis #automaticDifferentiation #uncertainty
-
🧐:
“It’s OK To Compare Floating-Points For Equality”, Nikita Lisitsa (https://lisyarus.github.io/blog/posts/its-ok-to-compare-floating-points-for-equality.html).
Via HN: https://news.ycombinator.com/item?id=47767398
On Lobsters: https://lobste.rs/s/l6c9wi/it_s_ok_compare_floating_points_for
#Math #Programming #FloatingPoint #NumericalAnalysis #Precision #Errors #Numerics
-
🧐:
“It’s OK To Compare Floating-Points For Equality”, Nikita Lisitsa (https://lisyarus.github.io/blog/posts/its-ok-to-compare-floating-points-for-equality.html).
Via HN: https://news.ycombinator.com/item?id=47767398
On Lobsters: https://lobste.rs/s/l6c9wi/it_s_ok_compare_floating_points_for
#Math #Programming #FloatingPoint #NumericalAnalysis #Precision #Errors #Numerics
-
🧐:
“It’s OK To Compare Floating-Points For Equality”, Nikita Lisitsa (https://lisyarus.github.io/blog/posts/its-ok-to-compare-floating-points-for-equality.html).
Via HN: https://news.ycombinator.com/item?id=47767398
On Lobsters: https://lobste.rs/s/l6c9wi/it_s_ok_compare_floating_points_for
#Math #Programming #FloatingPoint #NumericalAnalysis #Precision #Errors #Numerics
-
🧐:
“It’s OK To Compare Floating-Points For Equality”, Nikita Lisitsa (https://lisyarus.github.io/blog/posts/its-ok-to-compare-floating-points-for-equality.html).
Via HN: https://news.ycombinator.com/item?id=47767398
On Lobsters: https://lobste.rs/s/l6c9wi/it_s_ok_compare_floating_points_for
#Math #Programming #FloatingPoint #NumericalAnalysis #Precision #Errors #Numerics
-
Alright, future engineers!
Euler's Method: Approximates solutions to Ordinary Differential Equations (ODEs) by taking small steps along the tangent. Ex: `y_new = y_old + h * f(x_old, y_old)`. Pro-Tip: Simple, but smaller 'h' improves accuracy (at cost of computation)!
#ODEs #NumericalAnalysis #STEM #StudyNotes -
Alright, future engineers!
Euler's Method: Approximates solutions to Ordinary Differential Equations (ODEs) by taking small steps along the tangent. Ex: `y_new = y_old + h * f(x_old, y_old)`. Pro-Tip: Simple, but smaller 'h' improves accuracy (at cost of computation)!
#ODEs #NumericalAnalysis #STEM #StudyNotes -
Alright, future engineers!
Euler's Method: Approximates solutions to Ordinary Differential Equations (ODEs) by taking small steps along the tangent. Ex: `y_new = y_old + h * f(x_old, y_old)`. Pro-Tip: Simple, but smaller 'h' improves accuracy (at cost of computation)!
#ODEs #NumericalAnalysis #STEM #StudyNotes -
Alright, future engineers!
Euler's Method: Approximates solutions to Ordinary Differential Equations (ODEs) by taking small steps along the tangent. Ex: `y_new = y_old + h * f(x_old, y_old)`. Pro-Tip: Simple, but smaller 'h' improves accuracy (at cost of computation)!
#ODEs #NumericalAnalysis #STEM #StudyNotes -
Alright, future engineers!
Euler's Method: Approximates solutions to Ordinary Differential Equations (ODEs) by taking small steps along the tangent. Ex: `y_new = y_old + h * f(x_old, y_old)`. Pro-Tip: Simple, but smaller 'h' improves accuracy (at cost of computation)!
#ODEs #NumericalAnalysis #STEM #StudyNotes -
Truncation error is the error from approximating an exact solution (e.g., infinite series) with a finite one. Ex: `e^x ≈ 1 + x + x^2/2!` (we 'truncated' it!). Pro-Tip: Use more terms or smaller step sizes to reduce this error!
-
Truncation error is the error from approximating an exact solution (e.g., infinite series) with a finite one. Ex: `e^x ≈ 1 + x + x^2/2!` (we 'truncated' it!). Pro-Tip: Use more terms or smaller step sizes to reduce this error!
-
Truncation error is the error from approximating an exact solution (e.g., infinite series) with a finite one. Ex: `e^x ≈ 1 + x + x^2/2!` (we 'truncated' it!). Pro-Tip: Use more terms or smaller step sizes to reduce this error!
-
Truncation error is the error from approximating an exact solution (e.g., infinite series) with a finite one. Ex: `e^x ≈ 1 + x + x^2/2!` (we 'truncated' it!). Pro-Tip: Use more terms or smaller step sizes to reduce this error!
-
Truncation error is the error from approximating an exact solution (e.g., infinite series) with a finite one. Ex: `e^x ≈ 1 + x + x^2/2!` (we 'truncated' it!). Pro-Tip: Use more terms or smaller step sizes to reduce this error!
-
Your college professor teaches you "A-stable methods are required for stiff ODEs". But PSA, the most commonly used stiff ODE solvers (adaptive order BDF methods) are not A-stable. #sciml #numericalanalysis #diffeq
-
Your college professor teaches you "A-stable methods are required for stiff ODEs". But PSA, the most commonly used stiff ODE solvers (adaptive order BDF methods) are not A-stable. #sciml #numericalanalysis #diffeq
-
Your college professor teaches you "A-stable methods are required for stiff ODEs". But PSA, the most commonly used stiff ODE solvers (adaptive order BDF methods) are not A-stable. #sciml #numericalanalysis #diffeq
-
Your college professor teaches you "A-stable methods are required for stiff ODEs". But PSA, the most commonly used stiff ODE solvers (adaptive order BDF methods) are not A-stable. #sciml #numericalanalysis #diffeq
-
Your college professor teaches you "A-stable methods are required for stiff ODEs". But PSA, the most commonly used stiff ODE solvers (adaptive order BDF methods) are not A-stable. #sciml #numericalanalysis #diffeq
-
New preprint https://arxiv.org/abs/2511.06957
A #perspective discussing Moreau-Yosida (MY) techniques in #densityfunctionaltheory.
MY regularisation has enabled to import tools from #convexanalysis into #dft
providing a new mathematical understanding of the most important atomistic simulation approach
and new robust algorithms for Kohn-Sham #dft.Thanks to my co-authors from the #hylleraas centre and #oslomet for insightful discussions.
-
New preprint https://arxiv.org/abs/2511.06957
A #perspective discussing Moreau-Yosida (MY) techniques in #densityfunctionaltheory.
MY regularisation has enabled to import tools from #convexanalysis into #dft
providing a new mathematical understanding of the most important atomistic simulation approach
and new robust algorithms for Kohn-Sham #dft.Thanks to my co-authors from the #hylleraas centre and #oslomet for insightful discussions.
-
Automatic Differentiation Can Be Incorrect
#HackerNews #AutomaticDifferentiation #Incorrectness #NumericalAnalysis #Simulation #MachineLearning
-
Automatic Differentiation Can Be Incorrect
#HackerNews #AutomaticDifferentiation #Incorrectness #NumericalAnalysis #Simulation #MachineLearning
-
Automatic Differentiation Can Be Incorrect
#HackerNews #AutomaticDifferentiation #Incorrectness #NumericalAnalysis #Simulation #MachineLearning
-
Automatic Differentiation Can Be Incorrect
#HackerNews #AutomaticDifferentiation #Incorrectness #NumericalAnalysis #Simulation #MachineLearning
-
Automatic Differentiation Can Be Incorrect
#HackerNews #AutomaticDifferentiation #Incorrectness #NumericalAnalysis #Simulation #MachineLearning
-
Implicit Ode Solvers Are Not Universally More Robust Than Explicit Ode Solvers
#HackerNews #ImplicitOdeSolvers #ExplicitOdeSolvers #NumericalAnalysis #ComputationalMath #Robustness #Algorithms
-
Implicit Ode Solvers Are Not Universally More Robust Than Explicit Ode Solvers
#HackerNews #ImplicitOdeSolvers #ExplicitOdeSolvers #NumericalAnalysis #ComputationalMath #Robustness #Algorithms
-
Implicit Ode Solvers Are Not Universally More Robust Than Explicit Ode Solvers
#HackerNews #ImplicitOdeSolvers #ExplicitOdeSolvers #NumericalAnalysis #ComputationalMath #Robustness #Algorithms