The op amp differentiator is generally not used in any analog computer application. The basic reason for this is that high-frequency noise signals will not be suppressed by this circuit; rather they will be amplified far beyond the amplification of the desired signal.
In a computer there are many A/D converters that put analog into digital. This signal is what is usually then led into an op amp which in the right configuration can be designed into an integrator or differentiator which is then used to solve differential equations.
J. L Blue has written: 'B2DE' -- subject(s): Computer software, Differential equations, Elliptic, Differential equations, Nonlinear, Differential equations, Partial, Elliptic Differential equations, Nonlinear Differential equations, Partial Differential equations
There is no application of differential equation in computer science
Carl Dill has written: 'A computer graphic technique for finding numerical methods for ordinary differential equations' -- subject(s): Computer graphics, Differential equations.., Numerical calculations
The computer solves a very large system of partial differential equations.
Dennis G. Zill has written: 'A First Course in Differential Equations with Modeling Applications (Non-InfoTrac Version)' 'Pssm-Calculus' 'Multivariable calculus' -- subject(s): Calculus 'Even-numbered answers' 'Advanced engineering mathematics' -- subject(s): Engineering mathematics 'Advanced engineering mathematics' -- subject(s): Engineering mathematics 'Manual for differential equations with computer lab experiments' -- subject(s): Differential equations, Laboratory manuals, Data processing 'College Algebra and Trigonometry' -- subject(s): Trigonometry, Algebra 'Differential equations with boundary-value problems' -- subject(s): Differential equations, Textbooks, Boundary value problems 'Algebra and trigonometry' -- subject(s): Trigonometry, Algebra 'Pssm-Advanced Engineering Mathematics' 'College algebra' -- subject(s): Algebra 'Introd Calc F/Bus, Econ, Soc Sci' 'Student Solutions Manual for Zill's Differential Equations With Computer Lab Experiments' 'Differential equations with computer lab experiments' -- subject(s): Differential equations, Computer-assisted instruction 'Mathematica Mac Notebook-Diff Equ W/Comp' 'A First Course in Differential Equations' -- subject(s): Differential equations, Differentiaalvergelijkingen 'Maple IBM Notebook - Diff Equ W/Comp Lab'
J. R. Cash has written: 'Stable recursions' -- subject(s): Computer algorithms, Differential equations, Iterative methods (Mathematics), Numerical integration, Numerical solutions, Stiff computation (Differential equations)
C. William Gear has written: 'Introduction to computers, structured programming, and applications' 'Runge-Kutta starters for multistep methods' -- subject(s): Differential equations, Numerical solutions, Runga-Kutta formulas 'BASIC language manual' -- subject(s): BASIC (Computer program language) 'Applications and algorithms in science and engineering' -- subject(s): Data processing, Science, Engineering, Algorithms 'Future developments in stiff integration techniques' -- subject(s): Data processing, Differential equations, Nonlinear, Jacobians, Nonlinear Differential equations, Numerical integration, Numerical solutions 'ODEs, is there anything left to do?' -- subject(s): Differential equations, Numerical solutions, Data processing 'Computer applications and algorithms' -- subject(s): Computer algorithms, Computer programming, FORTRAN (Computer program language), Pascal (Computer program language), Algorithmes, PASCAL (Langage de programmation), Programmation (Informatique), Fortran (Langage de programmation) 'Method and initial stepsize selection in multistep ODE solvers' -- subject(s): Differential equations, Numerical solutions, Data processing 'Stability of variable-step methods for ordinary differential equations' -- subject(s): Differential equations, Numerical solutions, Convergence 'What do we need in programming languages for mathematical software?' -- subject(s): Programming languages (Electronic computers) 'Introduction to computer science' -- subject(s): Electronic digital computers, Electronic data processing 'PL/I and PL/C language manual' -- subject(s): PL/I (Computer program language), PL/C (Computer program language) 'Stability and convergence of variable order multistep methods' -- subject(s): Differential equations, Numerical solutions, Numerical analysis 'Unified modified divided difference implementation of Adams and BDF formulas' -- subject(s): Differential equations, Numerical solutions, Data processing 'Asymptotic estimation of errors and derivatives for the numerical solution of ordinary differential equations' -- subject(s): Differential equations, Numerical solutions, Error analysis (Mathematics), Estimation theory, Asymptotic expansions 'FORTRAN and WATFIV language manual' -- subject(s): FORTRAN IV (Computer program language) 'Computation and Cognition' 'Numerical integration of stiff ordinary differential equations' -- subject(s): Differential equations, Numerical solutions
John Malanchuk has written: 'Efficient algorithms for solving systems of ordinary differential equations for ecosystems modeling' -- subject(s): Computer programs, Differential equations, Numerical analysis, Ecology, System analysis, Numerical solutions
All the optimization problems in Computer Science have a predecessor analogue in continuous domain and they are generally expressed in the form of either functional differential equation or partial differential equation. A classic example is the Hamiltonian Jacobi Bellman equation which is the precursor of Bellman Ford algorithm in CS.
All the optimization problems in Computer Science have a predecessor analogue in continuous domain and they are generally expressed in the form of either functional differential equation or partial differential equation. A classic example is the Hamiltonian Jacobi Bellman equation which is the precursor of Bellman Ford algorithm in CS.
Analog computer science involves using continuous physical quantities to represent and process information. Key principles include using analog signals, circuits, and components to perform calculations and simulations. Applications include modeling complex systems, solving differential equations, and processing real-time data.